A WiMNet team, including Ph.D. student Craig Gutterman, undergraduate students Trey Gilliland, Brayn Fridman, and Yusheng Hu, and Prof. Zussman won the 3rd place in the ACM MMSys’20 Grand Challenge by Twitch. The Grand Challenge focused on Adaptation Algorithms for Near-Second Latency in video streaming.
The Grand Challenge is described as follows: “At Twitch, we have been successful in delivering ultra-low-latency streams to millions of viewers. However, as we drive towards near-second latency, we are finding that existing adaptation algorithms are not able to keep up – smaller player buffers do not provide enough time to respond to changing network conditions. We see this as a key challenge blocking the streaming community from reducing latency at scale.
The purpose of this challenge is to design an adaptation algorithm tailored towards HTTP chunked transfer streaming in the near-second (1-2s) latency range. It should minimize rebuffering while maximizing bandwidth utilization given the considerations above. The algorithm must also be fair to other clients viewing the same stream – its performance should not come at the expense of another. The proposed algorithm must be implementable on the web and within an HTML5-based player.”
The WiMNet team proposed a simple new adaptive bitrate (ABR) scheme, Stallion for STAndard Low-LAtency vIdeo cONtrol. Stallion uses a sliding window to measure the mean and standard deviation of both the bandwidth and latency. Stallion was compared to the standard DASH DYNAMIC algorithm over a variety of networking conditions, provided in the challenge, and demonstrated 1.8x increase in bitrate and 4.3x reduction in the number of stalls. A paper describing the solution as well as presentation and code are available below:
C. Gutterman, B. Fridman, T. Gilliland, Y. Hu, and G. Zussman, “Stallion: Video adaptation algorithm for low-latency video streaming,” in Proc. ACM MMSys’20, 2020. [download] [presentation] [code] [video]