Towards efficient multi-codec streaming

Yuriy Reznik; Karl Lillevold; Abhijith Jagannath; Nabajeet Barman

doi:10.1117/12.2633769

3 October 2022 Towards efficient multi-codec streaming

Yuriy Reznik, Karl Lillevold, Abhijith Jagannath, Nabajeet Barman

Proceedings Volume 12226, Applications of Digital Image Processing XLV; 1222607 (2022) https://doi.org/10.1117/12.2633769
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

One of the biggest challenges in modern-era streaming is the fragmentation of codec support across receiving devices. For example, modern Apple devices can decode and seamlessly switch between H.264/AVC and HEVC streams. Most new TVs and set-top boxes can also decode HEVC, but they cannot switch between HEVC and H.264/AVC streams. And there are still plenty of older devices/streaming clients that can only receive and decode H.264/AVC streams. With the arrival of next-generation codecs - such as AV1 and VVC, the fragmentation of codec support across devices becomes even more complex. This situation brings a question – how we can serve such a population of devices most efficiently by using codecs delivering the best performance in all cases yet producing the minimum possible number of streams and such that the overall cost of media delivery is minimal? In this paper, we explain how this problem can be formalized and solved at the stage of dynamic generation of encoding profiles for ABR streaming. The proposed solution is a generalization of contextaware encoding (CAE) class-of techniques, considering multiple sets of renditions generated using each codec and codec usage distributions by the population of the receiving devices. We also discuss several streaming system-level tools needed to make the proposed solution practically deployable.

Conference Presentation

Citation Download Citation

Yuriy Reznik, Karl Lillevold, Abhijith Jagannath, and Nabajeet Barman "Towards efficient multi-codec streaming", Proc. SPIE 12226, Applications of Digital Image Processing XLV, 1222607 (3 October 2022); https://doi.org/10.1117/12.2633769

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