Name
Abstract | ||
|---|---|---|
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.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3339825.3397042 | MMSys '20: 11th ACM Multimedia Systems Conference
Istanbul
Turkey
June, 2020 |
Keywords | DocType | ISBN |
Adaptive video streaming, low latency, online Optimization | Conference | 978-1-4503-6845-2 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Theo Karagkioules | 1 | 1 | 0.68 |
| Rufael Mekuria | 2 | 178 | 14.61 |
| Dirk Griffioen | 3 | 4 | 2.31 |
| Arjen Wagenaar | 4 | 1 | 0.68 |