Low-latency is a critical user Quality-of-Experience (QoE) metric for live video streaming. It poses significant challenges for streaming over the Internet. In this paper, we explore the design space of low-latency live video streaming by developing dynamic models and optimal control strategies. We further develop practical live video streaming algorithms within the Model Predictive Control (MPC) framework, namely MPC-Live, to maximize user QoE by adapting the video bitrate while maintaining low end-to-end video latency in dynamic network environment. Through extensive experiments driven by real network traces, we demonstrate that our live video streaming algorithms can improve the performance dramatically within latency range of two to five seconds.
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Stallion: video adaptation algorithm for low-latency video streaming
As video tra!c continues to dominate the Internet, interest in nearsecond low-latency streaming has increased. Existing low-latency
streaming platforms rely on using tens of seconds of video in the
bu"er to o"er a seamless experience. Striving for near-second latency requires the receiver to make quick decisions regarding the
download bitrate and the playback speed. To cope with the challenges, we design a 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. We evaluate Stallion and compare it
to the standard DASH DYNAMIC algorithm over a variety of networking conditions. Stallion shows 1.8x increase in bitrate, and 4.3x
reduction in the number of stalls.
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- Award ID(s):
- 1910757
- PAR ID:
- 10218466
- Date Published:
- Journal Name:
- in Proc. ACM MMSys'20, 2020
- Page Range / eLocation ID:
- 327 to 332
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3339825.3397044
