Online learning for low-latency adaptive streaming
| Autor: | Arjen Wagenaar, Theo Karagkioules, Dirk Griffioen, Rufael Mekuria |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Real-time computing Latency (audio) 020206 networking & telecommunications Throughput 02 engineering and technology Modular design Convex optimization 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality of experience Latency (engineering) business Adaptation (computer science) Communication channel |
| Zdroj: | MMSys |
| DOI: | 10.1145/3339825.3397042 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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