1 research outputs found
Optimizing Adaptive Video Streaming in Mobile Networks via Online Learning
In this paper, we propose a novel algorithm for video rate adaptation in HTTP
Adaptive Streaming (HAS), based on online learning. The proposed algorithm,
named Learn2Adapt (L2A), is shown to provide a robust rate adaptation strategy
which, unlike most of the state-of-the-art techniques, does not require
parameter tuning, channel model assumptions or application-specific
adjustments. These properties make it very suitable for mobile users, who
typically experience fast variations in channel characteristics. Simulations
show that L2A improves on the overall Quality of Experience (QoE) and in
particular the average streaming rate, a result obtained independently of the
channel and application scenarios.Comment: 9 pages, 3 figures, submitted to IEEE Transactions on Multimedia
(under review