Online learning for low-latency adaptive streaming

May 2020
Theodoros Karagkioules, Rufael Mekuria, Dirk Griffioen, Arjen Wagenaar
Read paper

Achieving low-latency is paramount for live streaming scenarios, that are now-days becoming increasingly popular. In this paper, we propose a novel algorithm for bitrate adaptation in HTTP Adaptive Streaming (HAS), based on Online Convex Optimization (OCO). The proposed algorithm, named Learn2Adapt-LowLatency (L2A-LL), is shown to provide a robust adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions, throughput estimation or application-specific adjustments. These properties make it very suitable for users who typically experience fast variations in channel characteristics. The proposed algorithm has been implemented in DASH-IF's reference video player (dash.js) and has been made publicly available for research purposes at [22]. Real experiments show that L2A-LL reduces latency significantly, while providing a high average streaming bit-rate, without impairing the overall Quality of Experience (QoE); a result that is independent of the channel and application scenarios. The presented optimization framework, is robust due to its design principle; its ability to learn and allows for modular QoE prioritization, while it facilitates easy adjustments to consider applications beyond live streaming and/or multiple user classes.

Publication: MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference

* Won Grand Challenge on Adaptation Algorithms for Near-Second Latency"  organized by Twitch.