Geo-popularity assisted optimized transcoding for large scale adaptive streaming

Yao-Chung Lin; Chao Chen; Balu Adsumilli; Anil Kokaram; Steve Benting

doi:10.1117/12.2322191

17 September 2018 Geo-popularity assisted optimized transcoding for large scale adaptive streaming

Yao-Chung Lin, Chao Chen, Balu Adsumilli, Anil Kokaram, Steve Benting

Proceedings Volume 10752, Applications of Digital Image Processing XLI; 107520R (2018) https://doi.org/10.1117/12.2322191
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

HTTP-based video streaming techniques have now been widely deployed to deliver video streams over communication networks. With these techniques, a video player can dynamically select a video stream from a set of pre-encoded representations of the video source based on its available bandwidth and viewport size. The bitrates of the encoded representations thus determine the video quality presented to viewers and also the averaged streaming bitrate which is highly related to streaming cost for massive video streaming platforms. Our work minimizes the average streaming bitrate on a per-chunk basis by modeling the probability that a player observes a particular representation. Since popularity of videos is regional, this paper exploits a further optimization that uses regional statistics of client bandwidth and viewport instead of the global statistics. Simulation results demonstrate that using regional statistics reduces streaming cost for low-bandwidth regions while improving the delivered quality for high-bandwidth regions compared to a baseline configuration that uses global statistics.

Conference Presentation

Citation Download Citation

Yao-Chung Lin, Chao Chen, Balu Adsumilli, Anil Kokaram, and Steve Benting "Geo-popularity assisted optimized transcoding for large scale adaptive streaming", Proc. SPIE 10752, Applications of Digital Image Processing XLI, 107520R (17 September 2018); https://doi.org/10.1117/12.2322191

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