Table of Contents

Introduction

The story of Bitmovin began with video research and innovation back in 2012, when our co-founders Stefan Lederer and Christopher Mueller were students at Alpen-Adria-Universität (AAU) Klagenfurt. Together with their professor Dr. Christian Timmerer, the three co-founded Bitmovin in 2013, with their research providing the foundation for Bitmovin’s groundbreaking MPEG-DASH player and Per-Title Encoding. Five years later in 2018, a joint project between Bitmovin and AAU called ATHENA was formed, with a new laboratory and research program that would be led by Dr. Timmerer. The aim of ATHENA was to research and develop new approaches, tools and evaluations for all areas of HTTP adaptive streaming, including encoding, delivery, playback and end-to-end quality of experience (QoE). Bitmovin could then take advantage of the knowledge gained to further innovate and enhance its products and services. In the late spring and summer of 2023, the first cohort of ATHENA PhD students completed their projects and successfully defended their dissertations. This post will highlight their work and its potential applications. 

Video Research Projects

Optimizing QoE and Latency of Live Video Streaming Using Edge Computing and In-Network Intelligence

Dr. Alireza Erfanian

The work of Dr. Erfanian focused on leveraging edge computing and in-network intelligence to enhance the QoE and reduce end-to-end latency in live ABR streaming. The research also addresses improving transcoding performance and optimizing costs associated with running live streaming services and network backhaul utilization. 

1. Optimizing resource utilization – Two new methods ORAVA and OSCAR, utilize edge computing, network function virtualization, and software-defined networking (SDN). At the network’s edge, virtual reverse proxies collect clients’ requests and send them to an SDN controller, which creates a multicast tree to deliver the highest requested bitrate efficiently. This approach minimizes streaming cost and resource utilization while considering delay constraints. ORAVA, a cost-aware approach, and OSCAR, an SDN-based live video streaming method, collectively save up to 65% bandwidth compared to state-of-the-art approaches, reducing OpenFlow commands by up to 78% and 82%, respectively.
2. Light-Weight Transcoding – These three new approaches utilize edge computing and network function virtualization to significantly improve transcoding efficiency. LwTE is a novel light-weight transcoding approach at the edge that saves time and computational resources by storing optimal results as metadata during the encoding process. It employs store and transcode policies based on popularity, caching popular segments at the edge. CD-LwTE extends LwTE by proposing Cost- and Delay-aware Light-weight Transcoding at the Edge, considering resource constraints, introducing a fetch policy, and minimizing total cost and serving delay for each segment/bitrate. LwTE-Live investigates the cost efficiency of LwTE in live streaming, leveraging the approach to save bandwidth in the backhaul network. Evaluation results demonstrate LwTE processes transcoding at least 80% faster, while CD-LwTE reduces transcoding time by up to 97%, decreases streaming costs by up to 75%, and reduces delay by up to 48% compared to state-of-the-art approaches.

Slides and more detail

Video Coding Enhancements for HTTP Adaptive Streaming using Machine Learning

Dr. Ekrem Çetinkaya

The research of Dr. Çetinkaya involved several applications of machine learning techniques for improving the video coding process across 4 categories:

1. Fast Multi-Rate Encoding with Machine Learning – These two techniques address the challenge of encoding multiple representations of a video for ABR streaming. FaME-ML utilizes convolutional neural networks to guide encoding decisions, reducing parallel encoding time by 41%. FaRes-ML extends this approach to multi-resolution scenarios, achieving a 46% reduction in overall encoding time while preserving visual quality.
2. Enhancing Visual Quality on Mobile Devices – These three methods focused on improving visual quality on mobile devices with limited hardware. SR-ABR integrates super-resolution into adaptive bitrate selection, saving up to 43% bandwidth. LiDeR addresses computational complexity, achieving a 428% increase in execution speed while maintaining visual quality. MoViDNN facilitates the evaluation of machine learning solutions for enhanced visual quality on mobile devices.
3. Light-Field Image Coding with Super-Resolution – This new approach addresses the data size challenge of light field images in emerging media formats. LFC-SASR utilizes super-resolution to reduce data size by 54%, ensuring a more immersive experience while preserving visual quality.
4. Blind Visual Quality Assessment Using Vision Transformers – A new technique, BQ-ViT, tackles the blind visual quality assessment problem for videos. Leveraging the vision transformer architecture, BQ-ViT achieves a high correlation (0.895 PCC) in predicting video visual quality using only the encoded frames.

Slides and more detail

Policy-driven Dynamic HTTP Adaptive Streaming Player Environment

Dr. Minh Nguyen

The work of Dr. Ngyuen addressed critical issues impacting QoE in adaptive bitrate (ABR) streaming, with four main contributions:

1. Days of Future Past Plus (DoFP+) – This approach uses HTTP/3 features to enhance QoE by upgrading low-quality segments during streaming sessions, resulting in a 33% QoE improvement and a 16% reduction in downloaded data.
2. WISH ABR – This is a weighted sum model that allows users to customize their ABR switching algorithm by specifying preferences for parameters like data usage, stall events, and video quality. WISH considers throughput, buffer, and quality costs, enhancing QoE by up to 17.6% and reducing data usage by 36.4%.
3. WISH-SR – This is an ABR scheme that extends WISH by incorporating a lightweight Convolutional Neural Network (CNN) to improve video quality on high-end mobile devices. It can reduce downloaded data by up to 43% and enhance visual quality with client-side Super Resolution upscaling. 
4. New CMCD Approach – This new method for determining Common Media Client Data (CMCD) parameters, enables the server to generate suitable bitrate ladders based on clients’ device types and network conditions. This approach reduces downloaded data while improving QoE by up to 2.6 times

Slides and more detail  

Multi-access Edge Computing for Adaptive Video Streaming

Dr. Jesús Aguilar Armijo

The network plays a crucial role for video streaming QoE and one of the key technologies available on the network side is Multi-access Edge Computing (MEC). It has several key characteristics: computing power, storage, proximity to the clients and access to network and player metrics, that make it possible to deploy mechanisms at the MEC node to assist video streaming.

This thesis of Dr. Aguilar Armijo investigates how MEC capabilities can be leveraged to support video streaming delivery, specifically to improve the QoE, reduce latency or increase savings on storage and bandwidth. 

1. ANGELA Simulator – A new simulator is designed to test mechanisms supporting video streaming at the edge node. ANGELA addresses issues in state-of-the-art simulators by providing access to radio and player metrics, various multimedia content configurations, Adaptive Bitrate (ABR) algorithms at different network locations, and a range of evaluation metrics. Real 4G/5G network traces are used for radio layer simulation, offering realistic results. ANGELA demonstrates a significant simulation time reduction of 99.76% compared to the ns-3 simulator in a simple MEC mechanism scenario.
2. Dynamic Segment Repackaging at the Edge – The proposal suggests using the Common Media Application Format (CMAF) in the network’s backhaul, performing dynamic repackaging of content at the MEC node to match clients’ requested delivery formats. This approach aims to achieve bandwidth savings in the network’s backhaul and reduce storage costs at the server and edge side. Measurements indicate potential reductions in delivery latency under certain expected conditions.
3. Edge-Assisted Adaptation Schemes – Leveraging radio network and player metrics at the MEC node, two edge-assisted adaptation schemes are proposed. EADAS improves ABR decisions on-the-fly to enhance clients’ Quality of Experience (QoE) and fairness. ECAS-ML shifts the entire ABR algorithm logic to the edge, managing the tradeoff among bitrate, segment switches, and stalls through machine learning techniques. Evaluations show significant improvements in QoE and fairness for both schemes compared to various ABR algorithms.
4. Segment Prefetching and Caching at the Edge – Segment prefetching, a technique transmitting future video segments closer to the client before being requested, is explored at the MEC node. Different prefetching policies, utilizing resources and techniques such as Markov prediction, machine learning, transrating, and super-resolution, are proposed and evaluated. Results indicate that machine learning-based prefetching increases average bitrate while reducing stalls and extra bandwidth consumption, offering a promising approach to enhance overall performance.

Slides and more detail

Potential applications for Bitmovin products

The WISH ABR algorithm presented by Dr. Nguyen is already available in the Bitmovin Web Player SDK as of version 8.136.0, which was released in early October 2023. It can be enabled via AdaptationConfig.logic. Use of CMCD metadata is still gaining momentum throughout the industry, but Bitmovin and Akamai have already demonstrated a joint solution and the research above will help improve our implementation.

Bitmovin has experimented with server-side Super Resolution upscaling with some customers, mainly focusing on upscaling SD content to HD for viewing on TVs and larger monitors, but the techniques investigated by Dr. Çetinkaya take advantage of newer models that can extend Super Resolution to the client side on mobile devices. These have the potential to reduce data usage which is especially important to users with limited data plans and bandwidth. They can also improve QoE and visual quality while saving service providers on delivery costs. 

Controlling costs has been at or near the top of the list of challenges video developers and streaming service providers have faced over the past couple of years according to Bitmovin’s annual Video Developer Report. This trend will likely continue into 2024 and the resource management and transcoding efficiency improvements developed by Dr. Erfanian will help optimize and reduce operational costs for Bitmovin and its services. 

Edge computing is becoming more mainstream, with companies like Bitmovin partners Videon and Edgio delivering new applications that take advantage of available compute resources closer to the end user. The contributions developed by Dr. Aguilar Armijo address different facets of content delivery and provide a comprehensive approach to optimizing video streaming in edge computing environments. This has the potential to provide more actionable analytics data and enable more intelligent and robust adaptation during challenging network conditions. 

Conclusion

Bitmovin was born from research and innovation and 10 years later is still breaking new ground. We were honored to receive a Technology & Engineering Emmy Award for our efforts and remain committed to improving every part of the streaming experience. Whether it’s taking advantage of the latest machine learning capabilities or developing novel approaches for controlling costs, we’re excited for what the future holds. We’re also grateful for all of the researchers, engineers, technology partners and customers who have contributed along the way and look forward to the next 10 years of progress and innovation.

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The demand for high-quality video continues to grow at a steady pace

Streamed video content has been on a constant trajectory of growth over the course of the past 5-10 years. However, the demand for even more content, services, and supported devices ballooned throughout 2020 as a result of the COVID-19 pandemic, with an average market penetration rate of 93% for AVOD services, and 78% for SVOD services with nearly 949 million global users.
As the average consumer is expected to use an average of 5.5 different services at a given time, the competition to capture and keep their attention is incredibly high. So it’s the burden of modern streaming services (including less traditional ones like eLearning, Fitness, and religious institutions) to deliver a high-quality user experience. In today’s streaming environment, a high-quality experience (QoE) is influenced by a few key factors such as resolution, start-up time, buffer rates, error rates, and device reach. However, historically speaking achieving a high QoE is neither an easy, nor an inexpensive feat. But that’s not the case for long, not with technologies like Cloud-based Encoding that efficiently deliver high-quality content across nearly all device types and at any given bitrate.

What exactly is Per-Title Encoding?

Per-Title encoding is the process of encoding a video file that customizes a bitrate ladder based on the complexity of the selected content, with the specific goal of selecting the ideal bitrate that captures just enough information to present the optimal viewer experience. 

This is further optimized by a service’s selection of Video Codecs, all of which have their own variations of efficiency ratings – however, by applying a multi-codec approach (ex: A mix of HEVC and VP9) within the Per-Title workflow, one can reach up 99% of available devices.

For some context, organizations like Netflix, YouTube, Globo, and BBC are all using Per-Title encoding to increase the speed of their time-to-market, all while maximizing the quality of each piece of content. 
So where do cloud-based workflows fit in?

Generational Shift in Encoding Technologies

Up until roughly two years ago, nearly all content delivery workflows utilized hardware encoding solutions, this type of technology even dates back to the original transmission of black and white broadcast television. However, much like nearly every other SaaS solution, encoding has rapidly started shifting towards cloud-based solutions across the three major server providers, AWS, Azure, and Google Cloud Platform.
At the beginning of 2021, Bitmovin hosted a panel session featuring Streaming Media expert Dan Rayburn, alongside experts from Blizzard Entertainment and Sinclair Digital titled Encoding Workflows Best Practices, where they addressed the growing market of cloud-based encoding workflows:

“I’ve definitely seen a huge jump to cloud and transporting as a service and encoding as a service. The cost of operating a workflow is now based on a service per month instead of supporting it on-prem with your own engineers who need to do most of the building and support.” – Doug Bay, Sinclair Digital

Cloud-based workflows help enable organizations to scale their new apps and platforms with extensive potential customization based on their individual needs. This new methodology is consistently improving three primary factors of running an OTT service: Resource Availability, Time-to-Market, and Reducing Operational Costs.

Benefits of Cloud-based Encoding Workflows

1. Resource Availability – Moving encoding workflows to the cloud opens up countless engineering and support resources to focus on other more critical tasks like improving general user experience, implementing AI, or building more stable workflows.
2. Cost Reduction – The next major benefit of a Cloud-based workflow is a reduction in pure cost, building and sustaining your own hardware encoder and the server is an immense undertaking. Given that the cloud is shared by multiple organizations, you are no longer on the hook for owning your own expensive server. These costs are further reduced by an equally expanding market of cloud-service providers to sustain the high demand.

“The availability of the silent architecture allows more organizations to make the shift away from on-prem: firstly from a financial perspective, and secondly from a scalability standpoint – you can’t get this type of scale with the financial return in continuing with ground-based encoding factories.” – Corey Smith, Blizzard

3. Time-to-Market – Establishing a fully functional transcoding workflow on your own will take you months, especially since you have to devote those highly valuable engineering resources. Cloud-based infrastructures have preset templates that you just need to plug your content into, and you can carry on with your distribution, allowing organizations to publish VoD content almost immediately after the live broadcast concludes.

Today, most video encoding service providers offer a cloud-based solution in addition to the standard hardware-based solution. As of 2020, Bitmovin shifted to a cloud-only solution and offers operators to use their own cloud service, aptly called “Cloud Connect”.

Introducing Cloud Connect

The new Cloud Connect cloud-based encoding solution makes it possible to install Bitmovin’s API directly on your infrastructure, so you can ensure that private data never leaves your cloud data center. This yields three overarching benefits:

1. Data Privacy and Security – If the privacy and security of your video files are of paramount importance, Cloud Connect adds a layer of protection. While Bitmovin’s standard Managed Cloud supports Hollywood-grade security practices such as multi-DRM support for Microsoft PlayReady, Google Widevine, & Apple FairPlay Streaming, license key protection, watermarking, concurrency management, and more, data still needs be processed through Bitmovin’s API. With Cloud Connect, you can keep your video files inside your internal network throughout the entire encoding process. 
2. Cost – While Bitmovin’s Managed Cloud encoding API will save you money on bandwidth in the long run, you might be able to negotiate better hosting rates by installing Cloud Connect on your infrastructure directly. Due to competition in the cloud hosting ecosystem, large customers are often able to negotiate rock-bottom rates in exchange for long-term commitments.
3. Maintenance – Finally, it’s worth noting that when you use Bitmovin Cloud Connect, you’ll be bringing your own cloud account. Managed AWS, Azure, or GCP instances should require very little maintenance because Bitmovin’s software handles the auto-scaling and container provisioning for you. With Bitmovin Managed cloud, Bitmovin’s team will be responsible for configuring these instances, so you won’t have any direct input, but with Cloud Connect, you’ll have full control over your servers.

With an ever-increasing global demand for high-quality VoD apps and platforms, it’s imperative that streaming services within every industry work towards creating a highly efficient (while relatively inexpensive) backend video infrastructure.  
If you’d like to take a deeper dive into cloud connect encoding workflows, check out some of our other great content below:
Blog Series – Cloud-based Per-Title Encoding Workflows (with AWS)
Establishing the Architecture
Implementing the new Workflow
Adding a Player and Video Analytics

This article was featured in MEDIANTEK’s Summer 2021 Magazine. View the full magazine here

The post Delivering the Best Quality of Experience with Cloud-based Encoding appeared first on Bitmovin.

In addition, countless content owners have an enormous backlog of non-4K quality content that simply doesn’t seem right on a high-quality device. According to a recent study & webinar by Streaming Media, roughly 45% of content distributors utilize 4K UHD to bring premium content to market. With that in mind, it’s imperative that OTT providers find ways to deliver as much of their content in HD quality and higher. In today’s times of online streaming revolution that’s defined by hyper-dynamic Video Tech development, it’s possible to deliver older content into UHD quality content using 4K upscaling technologies (such as per-title encoding, upsampling, and super-resolution). 
In this article (featuring Teleport Media, a decentralized delivery services provider), we’re going to talk about 4K video as the new QoE standard and how some of our respective clients are achieving 4K quality distribution with the help of next-gen video optimization solutions without breaking the bank.

Status Quo: 4K Quality for Streaming Media

According to multiple industry reports, the current market penetration of 4K devices (or higher) such as Smart TVs, tablets, and mobile phones is valued at roughly $62B and is expected to grow to $213B by 2026. In addition, the market for 8K devices is expected to balloon 72 million sold devices (globally) by 2025, from a measly 350,000 in 2020.
According to a report by the European Audiovisual Observatory, Russia has rapidly become one of the largest growing consumer markets for 4K content (and devices) with HD channel (and OTT service) subscriptions reaching as high as 28 million households, with the expectation that 18.5 million of those households will own a UHD capable device by the year 2023.

A key example of content operators expanding their libraries to support and upscale 4K content is Bitmovin and Teleport Media’s joint customer, Okko multimedia service – one of the largest Russian VOD services, with a monthly viewership of 17 million households. To achieve the market’s demand for 4K content, Okko had to implement a back-end strategy that would equally leverage new incoming 4K content and upsample their existing library with a scalable and affordable video workflow.

How Okko is Upscaling Content to 4K

As the OTT streaming market continues to rapidly grow across Russia, Okko has taken aggressive action to improve the viewer experience of its users. Working with Bitmovin to implement the latest Dolby Vision experience as well as reworking existing content for an optimized 4K and HD viewing experience by implementing an adaptive bitrate ladder encoding methodology.
The adaptive bitrate ladder streaming methodology analyzes the elements of a video based on the type of content that’s being displayed to optimize intra-frame and inter-frame compression. The process compensates and optimizes motion, spatial and temporal compression for quality control all while minimizing bitrates. This enables organizations like Okko to deliver 4K by implementing the least lossy and most compressive encodes and automatically adapting video segments based on the available bandwidth of various regions. You can find a chart of the standard bitrate ladder below as compared to the ladder for Bitmovin’s per-title encoding solution:

By applying per-title encoding, Okko has been able to continuously provide UHD content for all of their native apps and web services, increasing the availability of premium content for customers across all of Russia. According to Okko CEO, Yana Bardintseva:

“We believe that new features and coding algorithms will help us to strengthen Okko’s position as a technological leader among VOD services and provide our clients with the best possible 4K visual quality on the market.” 

Bitrate Upscaling Methods

As you can imagine, just because a majority of modern stream-capable devices can deliver all of their content in 4K, doesn’t mean that they will. The limitations around delivering 4K content come from all angles – such as media files that aren’t natively in UHD resolution, bandwidth limitations due to poor connectivity, and even server & distribution capabilities from “outdated” infrastructures (4G and below networks). So, how can distributors overcome these limitations? With a mix of bitrate upscaling methods, such as per-title encoding, decentralized content delivery mechanisms, and future technologies like super-resolution.

Per-Title Encoding

The entire concept of bitrate upscaling is about the process of introducing higher bitrates without adding any hardware or making significant changes to video workflow. One way to do so is with the aforementioned per-title encoding method, wherein content is compressed efficiently and effectively enough to be delivered at (potentially) 4K quality resolution, regardless of bandwidth availability. This can be enabled by setting up a wider bitrate ladder that encompasses 4K content.

Decentralized Content Delivery

Another method of introducing higher bitrate capacity into a video workflow is by applying a decentralized CDN. This is a cloud-based service that manages the peer-to-peer network created out of the devices watching the same video, where each new viewer acts as an “edge mini-server” and video redistributive node. The peer-to-peer architecture positively affects the highest possible visual quality maintenance both from the tech side and the business side. 
The P2P architecture enables a higher delivery speed by splitting up encoded video chunks between multiple viewers in parallel, thereby lightening the buffer requirements on any given network. Given that 4K content takes up significantly more space than any other media file, spreading out the video chunks between reliable peers reduces the server load. 
Additionally, a decentralized CDN plugin to a video player acts like an intuitive load balancer that optimizes the cost of delivery by enabling networks to deliver 2-3x more traffic at the same price. Thus, content distributors can now deliver 4K quality at the cost of HD.

As up to 80% of traffic can pass through a P2P network, the server is no longer forced to downgrade the video and won’t get overloaded from the 4K content pulls. As a result, more viewers can consume the higher resolution content concurrently. The viewers got improved visual quality at the last mile despite OTT’s data centers capacity and available bandwidth. 

Super-resolution

With delivery infrastructures and bandwidth limitations removed with the combination of cloud-based encoding and P2P CDNs, organizations can look towards innovative technologies to start upsampling their older content. A relatively new methodology to upsampling older content is the practice of super-resolution (with machine-learning), where non-4K content is improved using a mathematical calculation to predict what an image will and should look like based on previous frames (otherwise known as Spatial Upsampling). Super-resolution is better than other forms of upsampling, as it adapts its predictions based on the content that’s been input (similar to per-title encoding).
Now if you combine all three methods of bitrate upscaling, you’ll be guaranteed to provide one of the best 4K video experiences available on the market.

Meet the market: Best QoE with Bitrate Upscaling

Bitmovin’s encoding and Teleport Media’s P2P CDN solutions both are designed to create the highest QoE with their focus on bitrate upscaling. The application of an adaptive bitrate streaming solution that’s delivered concurrently across countless peers will help any organization upgrade its lower-quality content and cope with the fierce market competition. In addition, content distributors will be able to reach the ever-growing market of 4K and 8K capable devices.

As UHD reach is expected to spread over 600 million households worldwide, it’s imperative that you deliver content that matches the quality of the device, be it with spatial upsampled content through super-resolution, with extended ABR ladder using Bitmovin’s Per-Title or 3Pass Encoding, and/or with Teleport Media’s decentralized P2P CDN solution.
If you’d like to learn more about per-title encoding, super-resolution, ABR ladders, P2P CDNs, and other video infrastructure technologies, check some of our additional resources below:
[Blog Series] Super-Resolution with Machine Learning:

• https://bitmovin.com/super-resolution-machine-learning-p1/
• https://bitmovin.com/super-resolution-machine-learning-p2/
• https://bitmovin.com/super-resolution-deployments-machine-learning-p3

[Blog Post] Video Compression Basics: Encoding Definition and Adaptive Bitrate
[Use Case] How to transform the quantity into quality and decrease buffering 50%
[Case Study] Globo is Setting 4K Standards with Per-Title Encoding
Contact Bitmovin 
Contact Teleport Media

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While interactive videos have existed in one form or another for almost two decades, they have changed considerably over the years. According to the 2021 video marketing report by Wyzowl, 24% of video marketers plan to include interactive video in their 2021 video marketing strategy. (+3% from last year) and it’s getting more common in other verticals as well. In fact, Marketing Dive found that interactive videos boost time spent viewing content by a whopping 47%.
One of the main reasons for the rise in interactive video content is that most people have access to broadband internet, but what else contributes to this trend? Are interactive videos increasing user quality of experience? Or are they just overhyped?
In this post, we’ll explore some of the trends in interactive video. We’ll see how brands are using interactive videos to create engaging content and explore the question of whether we can tie more interactive content to a better viewer quality of experience or not.

What is QoE?

“Quality of experience” or QoE is a measurement of how delighted a customer is with a specific service, in this case, an internet-streaming video. Theoretically, when a viewer enjoys a video, watch time, actions taken, and revenue generated from your content will increase. We already know that video quality affects QoE, but let’s look at how QoE is measured and then decide if there is a good way to tie these metrics to the data we have on interactive videos.

Measuring Quality of Experience

There are several ways to measure QoE, but it can be a bit of a challenge. When it comes to subjective metrics, the mean opinion score (MOS) can be useful. It’s based on human ratings, usually by asking viewers to rate the quality of their watching experience during or after streaming a video. 
However, MOS has its downsides. It’s difficult to automate its measurement, and in some cases, viewers give random ratings because it’s easier than clicking the “close” button. According to Twilio, people tend to avoid perfect ratings, which also affects the precision of MOS.
Another approach to measuring QoE is to use objective metrics quantified with a video analytics tool like Bitmovin’s. Playback start time, number and duration of interruptions, and video completion can all tell you something about whether the video provided the experience quality the user expected.

Types of Interactive Video

While it’s impossible to collect a perfect measurement of quality of experience, this gives you some idea of how we might figure out if interactive videos improve QoE. Before we look at the research, let’s look at some of the innovative interactive video experiences reaching consumers today.

Group Watching Experiences

With the Covid-19 pandemic limiting gatherings, group video watching has become an excellent way to watch videos with friends you cannot see in person. Services like Teleparty and Watch2Gether allow you to watch content at the same time and talk or chat about the movie.
Watching sports with friends has also changed in 2020. BT Sports and Yahoo! Sports both launched group watching experiences through their web and mobile applications. This means that fans can enjoy the social feeling of being at the game without the risk of spreading Coronavirus.

Customizable Videos

Customizable videos allow you to adjust some aspects of the video according to your preferences. For example, some streaming services will enable you to select the camera angle you want to watch during a sporting event.
This video of the hot lap in Spa-Francorchamps is an excellent example of an interactive video that includes a 360-degree camera of an F1 driver completing the circuit. Videos like this give you the sense of being right there in the driver’s seat!

Shoppable videos are very popular in eCommerce. These videos allow customers to watch someone use or wear a product, and if they like it, the customer can buy it directly from the video. This tactic can make even advertisements feel like fun.
Interactive shopping provides a huge opportunity for retailers to take advantage of the increase in online shopping this year. As an example, ColliShop – a Belgian department store – released a short video demonstrating some of their products and how they might be used together.

Branching Videos

In branching videos, viewers directly participate in the narrative by selecting the “path” they want their video adventure to take. A great example is this video by chocolate brand Callebaut. It allows you to choose between the type of chocolate, the base, and the filling you want, and then it shows you how the dessert is made.

Major motion pictures have also experimented with interactive video. To promote Will Smith’s movie, Focus, Warner Bros released an interactive trailer in which you can test your persuasion skills by making a series of attempts to con your mark.

Finally, there’s Honda’s unique interactive short video advertisement for the Civic Type R. The video starts with a guy living a typical life and driving a no-frills Honda Civic. When you press the “R” key on your keyboard, the video switches to a perfectly-synchronized version of the video with a guy who drives the sports model. Both videos are recorded from the same angles, but the sound and overall feeling are entirely different.

What the Data Says About Interactive Video

Most of the data available online supports the hypothesis that interactive videos significantly improve quality of experience. While it makes sense, let’s look at some of the research and how it points to this conclusion.
According to Wyzowl, 78% of marketers who use interactive videos say that it’s an effective marketing strategy. Studies also show that over 60% of viewers finish videos that have interactive content. With an interactive video, viewers must ask questions, make decisions, and perform tasks that keep them watching the video. This leads to 32% more memorable messages, meaning that people are likely to learn from or remember interactive videos.

But what about calls to action and conversions? Consumers might be watching interactive videos and spending more time on them, but that doesn’t necessarily mean they’re spending more money because of them.
According to Cinema8, shoppable videos have “conversion rates as high as 30%,” and average order sizes up to 40% higher than traditional e-commerce sites without video. The increase in engagement on interactive video directly improves conversions. Instead of a 1% conversion rate on banner and display ads, interactive videos tend to achieve 11% conversion rate!

Final Thoughts

The data so far indicate that interactive video improves viewer quality of experience. While most of the data we have today hasn’t undergone academic rigor, the piling on of positive signals seems to support this conclusion.
Interactive videos encourage users to watch for longer, pay closer attention to the content, and ultimately take action more often. As content producers get more creative and video encoding allows for fast high-quality streaming, we’ll likely see more innovation in interactive video.
If you’re looking for a platform to encode and host your interactive video, we can help. Bitmovin’s video encoding service offers industry-leading codecs that you can run in the cloud, and our video player can ensure viewers have a consistent experience across all platforms.

The post Do Interactive Experiences Increase Viewer Quality of Experience (QoE)? appeared first on Bitmovin.

The Broadcast Decision-Makers’ QoE Dilemma

The reality is that the adoption of any range of products and tools for video developers aimed at upgrading stream quality while improving bitrate expenditure can quickly lead to rising costs in your OTT workflows. As a decision-maker, you’re faced with the ultimate dilemma: How can I maintain the maximum quality of experience (QoE) without breaking the bank to build a successful OTT service?
What if there’s a way to have your cake and eat it too? Bitmovin and Teleport Media work hand-in-hand within the most complex and expensive part of your OTT supply chain (outside of content production) – during the storage and delivery phase. Our respective teams of video experts work every day to develop and improve software solutions that help to maximize your audience reach, improve the visual quality, and to achieve maximum cost-efficiency. 
In this article, we explain how to avoid broadcaster decision-makers’ dilemma by displaying how to implement next-gen video optimization solutions that reduce time to market, improve video quality, and prevent negative effects from video consumption spikes; thus gaining you more happy users, all while cutting costs on storage and delivery. 

Key drivers of video streaming costs outside content production

Video files often vary significantly in size based on two major factors, quality, and length, and most OTT services offer a variety of different types of content from short-form animations to long-form live-action films. Regardless of the type of content that an OTT service chooses to deliver to its consumer base, one of the most important things to consider for a budget is the amount of storage that the organization will need to run its business effectively and efficiently. 
The storage necessary to maintain a content library will always be multiplied against the number of viewers that a given Content Delivery Network (CDN) will distribute – naturally, higher resolution files will rack up a significant storage cost. Without proper management of a content library, an organization will quickly spend it’s full CDN bandwidth, thus limiting how many concurrent viewers can use your platform or service at a given moment.
This is most dangerous during unexpected traffic spikes for things viral content that will expend a pre-paid CDN plan (especially in scenarios where in-house CDN resources or multi-CDN strategies are limited), resulting in a costly purchase of additional impressions. A common market solution is to reduce the visual quality of the content, resulting in a much lower Quality of Experience (QoE) for the audience. Although cost-effective, visual quality reduction is one of the top reasons that your audience-base will churn in search of a higher QoE. Failing to reduce the size of your content will cause additional QoE issues like playback failures (slower start-up time, loading errors, buffering) or rebuffering.
In short, there are five main drivers of video streaming that can affect the total cost of operations (TCO):

1. File size
2. Storage capacity
3. CDN price
4. Visual quality/bitrates
5. Audience size

The ultimate challenge is how to address the first four drivers without losing customers and without massively increasing an OTT budget that could be reserved for content production and marketing efforts. This is where the true winning broadcasters can thrive – those that find and implement solutions that can redefine the viewer experience with low latency streaming, all while reducing their total cost of operations (TCO). Although generally interchangeable between organizations of all sizes, established platforms and services should seek to optimize their operations with an efficient and flexible video infrastructure; whereas new OTTs and broadcasters must focus on reducing their time-to-market and providing as high-quality content on as many devices as possible.
Bitmovin’s Adaptive Video Player, ML-enabled Cloud Encoding, and Video Analytic solutions paired with Teleport Media’s decentralized CDN deliver a unique workflow that reduces time-to-market and TCO, all while maintaining (if not improving) QoE.

Efficiently compressing content for QoE with Bitmovin

One of the most efficient methods any OTT organization can optimize their operations is with cloud-based per-title encoding. Per-title encoding is the method of customizing the bitrate ladder of individual videos based on the complexity of the content therein. The penultimate goal of any per title-based encode is to algorithmically select the optimal bitrate with a pre-defined codec that will deliver a perfect viewing experience without overspending on data delivery or storage.

This type of encoding is most ideal for larger content libraries or those with varying types of content by stripping away anything that’s beyond the human’s visual perception. To test which bitrate ladder is optimal for each piece of content, it’s recommended to use quality metrics like PSNR or SSIM. There are systems in place that use machine learning to automatically select the ideal bitrate, otherwise known as an Adaptive Bitrate Ladder (ABR). 
Alternatively – OTT services and broadcasters can opt towards multi-pass encoding techniques, which, as the name suggests, “simply” encode video files multiple times in the least lossy way possible. Although not as efficient as per-title encoding, multi-pass encoding offers the benefit of compression at scale and in bulk. Both options are great for optimizing your video workflows towards faster times to market and maintaining quality, but per-title encoding is better at over-all efficiency, whereas multi-pass is best suited for speed.
Once a video content library is compressed, a provider must find a way to efficiently deliver their content.

Decentralized video delivery with Teleport Media 

The next step to delivering high-quality content at scale is selecting a delivery method with a CDN provider. And much like top compression techniques, CDNs with efficiency, quality, and cost in mind are shifting to cloud-based architectures with decentralized delivery solutions. Based on the WebRTC technology, that has become a default feature of any modern internet-connected device, decentralized solutions like Teleport Media’s adaptive and secure peer-to-peer delivery system enables viewers’ devices to restream content between one another.

This method reduces delivery costs by vastly reducing the amount of traffic that actually flows from CDNs to viewers. The player-side P2P CDN architecture is protected from pirates and restreaming and is fully DRM compliant. Unlike a traditional CDN, the P2P content delivery network doesn’t contain any servers that create a bottleneck when the video has to be delivered at scale. It operates as a video player plug-in that takes the duty of traffic delivery from many sources simultaneously – both other viewers’ devices and the origin CDN. Teleport Media JS is compatible with a wide range of HTML5 video players, like Bitmovin’s player, and is designed for HTTP adaptive streaming. The P2P CDN works effectively in browser and mobile applications, on Live and VoD content.

Using Teleport Media with in-house and 3-party CDN

As traffic varies significantly throughout the day for almost any OTT video provider, it can be difficult to efficiently manage content delivery, especially during surges.

Teleport Media’s decentralized architecture is capable of leveraging the in-house CDN and making it serve 5-times more audience with the same amount of servers and bandwidth. It also decreases the overall costs of delivery, making the need for 3-party CDNs nearly vanish. When an OTT provider doesn’t own in-house CDN and uses delivery services from CDN vendors starting from the first byte of data, Teleport Media saves the budget up to 40% by providing much lower traffic delivery costs with the premium quality.

How decentralized CDN architecture lowers rebuffering

Rebuffering serves as a good indicator of troubles on in-house or external CDN. The more the audience, the less bandwidth per one viewer, the higher the chances of rebuffering.
Teleport Media provides each viewer with multiple connections to other peers and constantly measures their quality. If for any reason P2P connections slow down and put the video player at the risk of rebuffering, the viewer is switched to origin CDN until restoring full buffer.

Cost-Efficient End-to-End Content Delivery with Bitmovin and Teleport Media

The combination of Bitmovin’s per-title (or multi-pass) encoding solutions and Teleport Media’s decentralized CDN architecture are guaranteed to yield the optimal viewer QoE while reducing the cost operations without having to compromise the content’s resolution or disrupting an existing video infrastructure. The adaptively and least lossy compressed content will be delivered to nearly every user on every device. By combining Bitmovin’s and Teleport Media’s solutions you’ll get:

• Higher-quality content at lower bitrates (even for users in regions with low bandwidth capacity)
• Lower storage volume with compressed & re-streamed content using adaptive bitrate ladder renditions
• Top-quality playback during traffic spikes of any capacity

This was most recently displayed by Russian VoD Service, Okko.TV that was able to scale their content delivery to support 4K and UHD content across a wide span of devices. To further lower the complexity of implementation, Bitmovin recently adopted a direct plug-in to it’s HTML5 web-player.
If you’d like to further discuss how to improve visual quality while cutting storage and delivery costs, do reach out:

• Contact Teleport Media
• Contact Bitmovin

The post How to Improve Viewers’ Quality of Experience (QoE) While Cutting Storage and Delivery costs (ft. Teleport Media) appeared first on Bitmovin.

This is the third and final part of our series on high quality video streaming where we discuss the results of our experiments, our findings and conclusions that can be drawn from the data, particularly in relation to adaptive streaming with MPEG-DASH.

Experimental Results

The average media throughput in terms of bitrate [kbps] is shown in Figure 3. The “Available Bandwidth” on the left side of the figure shows the average bandwidth according to the predefined bandwidth trajectory used in the evaluation. The “Measured Bandwidth” by the clients is shown next to it, which is typically a bit lower than the available bandwidth due to the network overhead. The results of the different adaptation logics is shown subsequently and Bitmovin’s Adaptive Streaming Player (formerly known as the bitdash player)  – on the very right side of the figure – is among the top three implementations, namely 1. OSMF (1170.65 kbps), 2.Liu (1129.69 kbps), and 3. Bitmovin (bitdash) (1109.43 kbps). However, taking into account the average media throughput only is a fallacy when investigating the number of stalls as depicted in Figure 4. Interestingly, among the top three, only the Bitmovin player does not produce any stall, whereas the client with the best average media throughput produces the highest number of stalls – obviously not good for high QoE.
In addition to the overall results, we show a comparison of Bitmovin and DASH-JS – http://dash.itec.aau.at, one of the first DASH implementations – which is depicted in Figure 5. The figure shows both the average media throughput/bitrate and buffer level for the two implementations. The green line represents the buffer level showing the three underruns/stalls for DASH-JS where it falls below the autopause level (two at the beginning and one at the end) that causes the player to stop the playout and enter into a re-buffer phase. Bitmovin’s player does not cause any playback interruptions and is able to maintain a stable buffer level that corresponds to the available bandwidth. The red line shows the selected quality and both implementations are able to react very accurately according to the available bandwidth.

For the subjective evaluation, in total 220 microworkers participated in the subjective quality assessment from which 19 participants were excluded from the evaluation (due to issues during the crowdsourcing test as outlined in Section 3.3). From the remaining 201 participants, 143 were male and 58 female with an average age of 28. The results presented in this section reflect the behavior of the adaptation logics in a real-world environment with subjects spread across Europe accessing the test sequences over the open Internet.
Figure 6 depicts the QoE in terms Mean Opinion Score (MOS) per adaptation logic (95% confidence interval). Interestingly, DASH-JS (and also Instant) provides the highest MOS value but due to overlapping confidence intervals, relatively little can be stated as to whether it performs significantly better than the other algorithms. However, it provides a good indication regarding its effectiveness in a real-world environment. OSMF does not have the lowest MOS value despite its worse performance during the objective evaluation. In particular, Thang has the lowest MOS value although – during the objective evaluation – it does not cause any stalls but comes with a relatively low media throughput for both segment sizes.
Finally, we would like to share insights from a different study comparing DASH-JS, dash.js (DASH-IF reference player available at http://dashif.org/), and YouTube based on the work by Rainer et al.
Figure 7 shows an overview of the results along four dimensions: average representation bitrate (i.e., media throughput at the client), average startup time (or startup delay), average number of stalls, and the QoE in terms of Mean Opinion Score (MOS). DASH-JS maintains the lowest number of stalls (0.5 stalls on average) and the average representation bitrate is about 1,330 kbit/s. However, DASH-JS has the highest average startup time. The reason for this high startup time is that DASH-JS estimates the initial bandwidth when downloading the MPD and, thus, may select a higher bitrate in the be- ginning than the other clients. dash.js is outperformed by the other two DASH-enabled Web clients in three of the four dimensions. In particular, dash.js provides the lowest average representation bitrate, the highest number of stalls, and the lowest QoE. YouTube outperforms all other clients in three cases, specifically in the representation bitrate, startup time, and QoE. Furthermore, Figure 7 shows a correlation between the number of stalls and the QoE and that the representation bitrate also impacts the QoE, but is not solely responsible for the QoE.

Conclusions on Quality of Experience and MPEG-DASH

In this series of articles we have presented means for the Quality of Experience (QoE) evaluation of MPEG-DASH clients using objective/subjective measures and in controlled/real-world environments. An important finding is that the average media throughput/bitrate at the client cannot be used alone to describe the performance of MPEG-DASH clients and needs to be combined with other metrics such as the number of stalls. Interestingly, the start-up delay does not necessarily influence the QoE but buffer under-runs or stalls will definitely and also significantly impact the media experience and, thus, shall be totally avoided.
The findings presented in this article provide useful insights for current and future deployments of adaptive media streaming services based on the MPEG-DASH standard.

The post QoE with High Definition Video Through Adaptive Streaming with MPEG-DASH – 3 appeared first on Bitmovin.

Video is ubiquitous on the internet and everybody knows that. But the CISCO Visual Networking Index (VNI) quantifies it. Currently online video consumption accounts for 64% of global internet traffic and is projected to grow to 80% by 2019. This video traffic is driven by online video services such as Netflix and YouTube, but this is just the tip of the iceberg. Thousands of services similar to Netflix exist in the US alone, not to mention world wide. Millions of websites monetize video through ads or subscriptions. For example, websites with educational videos that show people how to use a specific software such as Photoshop or describe Roger Federer’s forehand, gaming platforms that show live games and walkthroughs, websites where people can learn how to cook or fix a car – every major broadcaster provides video on the internet.

Video is now also becoming heavily used in marketing. Video is probably the most powerful tool of communication and used by many more kinds of websites. Video is no longer a niche form of marketing. It is now a central part of marketing strategy and 81% of companies are already producing video content for their website. For example companies are using videos to explain their products, showing step by step instructions, or telling their brand story and introducing customers to their business. These are just a few examples. Videos are simply everywhere and there is a good reason for that:

• video increases engagement
• video increases session duration
• video increases conversions

Marketers learned to love video as it is the future of content and product marketing. comScore for example, reports that website visitors who have watched a video are 64% more likely to buy an online product and stay 2 minutes longer on average. According to a study from Usurv, a UK market research company, users are much more likely to share (39%), comment (36%) and like (56%) an online video compared to a text blog article. There are many studies that show similar effects when using video on your website, and marketers are already shifting their budgets in the online video direction. 68% of marketers and agency executives expect their online video budgets to increase in the next 12 months.

Startup Delay

Buffering

Buffering is annoying and everybody knows the situation when buffering occurs right at the moment when a touchdown, soccer goal, or the thrilling moment in a movie happens. Conviva’s annual Viewer Experience Report shows year after year, that users are becoming less tolerant of buffering. While in 2011, 1% of buffering lead to an average decrease of 3 minutes in viewing time, users are today much more sensitive and their patience regarding acceptance of buffering is decreasing. As a consequence, in 2014 1% of buffering lead to an average decrease of 14 minutes in viewing time.

Similar findings are shown by the University of Massachussetts, Amherst and Akamai. They compared viewers in their dataset that watched the exact same content, with the only difference being that one experienced buffering and the other did not. On average, a viewer that experienced 1% buffering watched 5.02% less of the video with a general upward trend with each additional percent of buffering.

Low Quality

We are all used to traditional broadcast video quality and we expect that video on the internet should have at least the same quality. Conviva conducted a study in March 2015 and asked 400 UK consumers about their attitude towards watching TV delivered over the Internet. Today’s viewers are looking for a TV-like experience and do not show much patience if this promise is broken due to web video problems. The study shows that 6 out of 10 viewers pay for a subscription VOD service while the key differentiator for the decision of which one they choose is viewing experience.

Conclusion

Everybody knows these problems and has experienced the frustration that these problems cause. However, we tend to forget how important it is to encode and prepare our videos in a way that these problems do not occur. If you remember one thing from this article, then it should be that it is critical for your business to encode and prepare your videos for the web properly.
These video problems trigger a decision from your users as to whether they should wait if things improve or leave and move on with the next activity. This decision is quantified by several studies and everybody makes that decision subconsciously, based also on previous experiences on the same website. Due to that fact that content providers are loosing viewers engagement and in the end money as their videos can not be monetized as effectively through ads, subscriptions, etc., marketers are loosing users and users engagement, which in the end decreases conversion and revenue on their businesses, as products and services cannot be sold as effectively. And by the way users get frustrated too ;).

What Can You Do?

You need to encode your videos in a way that they play everywhere, on every device, with low startup delay, with no buffering and in the best possible quality. We help you in doing so, as we use cutting edge standards for our output format such as MPEG-DASH and Apple HLS. But just using MPEG-DASH and HLS is not enough. You can implement these standards pretty badly and still get all the major video problems. For example it is possible to provide just one video quality, e.g., 1080p@4Mbps, with MPEG-DASH and HLS, which would then lead to very high startup delays, buffering and in the end a bad experience for many of your users. Just think about a user with a mobile device that is connected through 3G. This user would effectively buffer endlessly on such a stream. But not only users with low bandwidth connections would suffer on that stream; also users with high bandwidth fixed access would be affected by buffering or long startup delays. The internet works on a best effort basis and bandwidth will always vary and could also drop to very low speeds. That is typically the situation when video streams start to buffer. So just using MPEG-DASH or HLS is not enough. These standards promise video streaming with lowest startup delay and no buffering, in the best possible quality, but actually it depends on the implementation of the standard.

The way Bitmovin implements the MPEG-DASH standard is industry leading – we deliver what MPEG-DASH promises – video streaming with lowest startup delay and no buffering, in the best possible quality. Our implementation of MPEG-DASH delivers content with the lowest startup delay going down to 100ms from page load to first frame. Content created with Bitmovin plays without buffering and in the best possible quality. Specifically, Bitmovin provides 100% better quality without buffering compared to the next best industry solution. Bitmovin’s product line consists of a cloud based video encoding service and an HTML5 adaptive streaming player. These two products can be used independently, but you get the best possible experience when both are used together.
You can try our products for free. Just sign up and encode your videos properly for the web with our free plan. Embed the videos into your website and use our HTML5 player for the playback, and your users will never suffer from slow video startup, buffering and bad quality again!
Resources:

• Video Encoding Datasheet
• Video Player Datasheet
• Video Analytics Datasheet

The post The Video Problem: 3 Reasons Why Users Leave a Website with Badly Implemented Video appeared first on Bitmovin.

This is part one of a three part series analysing the impact of Adaptive Streaming, specifically MPEG-DASH, on Quality of Experience (QoE).

Real-time entertainment services such as high quality video streaming currently account for more than 60% of the Internet traffic, e.g., in North America’s fixed access networks as shown in Figure 1. Interestingly, these services are all delivery over-the-top (OTT) of the existing networking infrastructure using the Hypertext Transfer Protocol (HTTP) which resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (DASH). The MPEG-DASH standard enables smooth multimedia streaming towards heterogeneous devices and commonly assumes the usage of HTTP-URLs to identify the segments available for the clients.

Internet TV vs. Traditional TV in 2010

The following areas are most important to an overall TV experience:

• content
• timing control
• quality
• ease of use

Traditional TV surpasses Internet TV not only in quality, but also delivers a better overall experience.

Source: Cisco IBSG Youth Survey, Cisco IBSG Youth Focus Group Sessions, 2010
In the first part of this blog post we focus on the Quality of Experience (QoE) of DASH-based services. We provide a general definition of QoE and which parameters are important for media services based on MPEG-DASH. The second part of the blog post comprises results of a QoE evaluation of different adaptation logics proposed in the research literature and also one commercially available implementation from Bitmovin.

QOE for Adaptive Streaming with DASH

I. Quality of Experience

The term Quality of Experience (QoE) can be seen as an evolution from the term Quality of Service (QoS), both defined by the ITU-T in P.10/G.100. QoS is defined as the “totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied needs of the user of the service” whereas QoE is defined as “the overall acceptability of an application or service, as perceived subjectively by the end-user”. Although this definition was largely used (but not necessarily agreed upon), one could easily understand that acceptability is only one aspect of quality, as one may accept a service – depending on the context – but not necessarily be happy or satisfied. Therefore, the COST Action IC1003 – QUALINET goes a step beyond and defines QoE as “the degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations with respect to the utility and/or enjoyment of the application or service in the light of the user’s personality and current state”.
The QUALINET white paper goes even further and defines influence factors as “any characteristic of a user, system, service, application, or context whose actual state or setting may have influence on the Quality of Experience for the user” which are grouped into human, system, and context influence factors. Additionally, features of QoE are provided depending on the level of direct perception, interaction, the usage situation, and service. A QoE feature is thus defined as “a perceivable, recognized and namable characteristic of the individual’s experience of a service which contributes to its quality”.
As the definitions above are very generic, we will describe next what it means for DASH-based and high quality video streaming services.

II. QoE parameters for DASH

Different application domains may have different requirements in terms of QoE. Therefore, there is a need to provide specializations of a generally agreed definition of QoE (see above) pertaining to the respective application domain, taking into account its requirements formulated by means of influence factors and features of QoE. Consequently, an application-specific QoE definition can be provided by selecting the influence factors and features of QoE reflecting the requirements of the application domain and incorporating them into the generally agreed definition of QoE.
For DASH-based adaptive streaming services the main QoE influence factors can be described as

• initial/start-up delay,
• buffer underruns also known as stalls,
• quality switches and
• media throughput.

Initial/Start-up delay

The initial or start-up delay comprises the time between service/content request and start of the actual playout which typically involves processing time both at the server and client, network time for sending the MPD request and receiving first segments and initial buffer time before the playout starts. In general, the start-up delay should be low but it also depends on the use case. For example, the QoE of live streams or short movie clips is more sensitive to start-up delay than full-length video on demand content.

Buffer underruns / stalls

A stall occurs when the video/picture freezes. This is typically due to buffer underuns and playback is resumed if enough segments have been re-buffered. In practice, users experiencing stalls usually report a very low QoE and, thus, stalls should be completely avoided, even if it means increasing the start-up delay.

Quality switches

Under changing network conditions, quality switches occur to avoid buffer underruns (and stalls) in order to guarantee a smooth video playback. However, if it happens too often (e.g., every second) or with a high amplitude (e.g., switching from a very high quality to a very low quality representation) it may negatively impact the QoE.

Media throughput

Finally, the overall media throughput at the client, measured in media bits per second, and a higher media throughput, usually means higher QoE. But it should be never used alone but always in conjunction with the above metrics, as we will see in the experiment results.

Overview and Summary

The above-mentioned parameters focus on the context; specifically on delivery and device characteristics. However, QoE is about the users consuming content and services. Therefore it is important to understand how the content is provided for DASH-based adaptive streaming services, as it directly influences the QoE. In particular, this means how many different representations are available and in which qualities (incl. bitrate, resolution, etc.) and the actual segment length (e.g., 2s vs. 10s). Additional parameters are the available languages, existence of subtitles, closed caption, or any other means that help impaired users to consume the content more conveniently. In this paper we focus on the context parameters, different segment lengths, and assume a broad range of different representations available from which the client can select.

The second part of this blog post will provide results of a QoE evaluation of different adaptation logics proposed in the research literature and also one commercially available implementation from Bitmovin.

Read part 2 now!

Follow CIO Christian Timmerer on Twitter: @timse7
Follow Bitmovin on Twitter: @bitmovin

Video technology guides and articles

• Back to Basics: Guide to the HTML5 Video Tag 
• What is a VoD Platform? A comprehensive guide to Video on Demand (VOD)
• Video Technology [2022]: Top 5 video technology trends
• HEVC vs VP9: Modern codecs comparison
• What is the AV1 Codec?
• Video Compression: Encoding Definition and Adaptive Bitrate
• What is adaptive bitrate streaming
• MP4 vs MKV: Battle of the Video Formats
• AVOD vs SVOD; the “fall” of SVOD and Rise of AVOD & TVOD (Video Tech Trends)
• MPEG-DASH (Dynamic Adaptive Streaming over HTTP)
• Container Formats: The 4 most common container formats and why they matter to you.
• Quality of Experience (QoE) in Video Technology [2022 Guide]

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