Edge-Caching By The Numbers

There has been an on-going debate about the benefit of caching at the edge. For most operator networks, it can be costly to deploy servers at the edge without a clear demonstration of value. Consider the answer to the question, “will I get enough improvement

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Analyzing The Impact of Network Congestion Control On AR/VR/XR Streaming QoE

Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), and other forms of Extended Reality (XR), are beginning to drive deep immersive experiences. For example, AR/VR have reached a broad range of applications across various industry verticals including sports & entertainment, healthcare, automotive, aviation,

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How Cognitive AI is Enhancing Entertainment Experiences

As technology advances, so too does the way we experience entertainment. Artificial Intelligence (AI) has become an integral part of how we access and interact with our favorite films and shows. AI is increasingly being used to help viewers navigate their entertainment experiences, improve content

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Investigating Approaches to Multi-CDN Delivery

In this project, the SVTA Players and Playback group will look at the different approaches to multi-CDN delivery and provide an analysis to help streaming platforms select the best option for their specific business requirements. In addition, the group will propose a client-side solution using a JavaScript load balancer and SDK compatibility layer. This solution may encompass different heuristics for CDN selection, and one of our goals is to study the impact of client-side CDN switching on a client’s rate-adaptation algorithm.

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SVTA Distributed Tracing: Working Towards Improved Observability and QoS/QoE

As the streaming tech stack has grown in complexity, ensuring a high-quality viewing experience has become increasingly challenging. Part of that is the nature of a distributed architecture frequently assembled from third party services and technologies as well as just more moving parts. But part of it is also a lack of well-established standards which meet the specific needs of streaming content providers. The combination of these two issues results in operational “blind spots” when tracing issues through the workflow. Because of the critical importance of data within streaming operations, any blind spots can significantly increase the Mean Time to Diagnose (MTTD) and, more importantly, the Mean Time to Resolve (MTTR). When these operational streaming metrics go up, QoE and viewer satisfaction goes down often resulting in loss of revenue and/or increased churn. There is a need then, for a standardized approach to trace data across the different vendors in the streaming video technology stack with an obvious starting point: CDNs.

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