Optimizing Adaptive Video Streaming in Mobile Networks via Online Learning
This addresses video streaming quality for mobile users, offering a robust solution that is incremental in improving existing adaptive streaming methods.
The paper tackles the problem of video rate adaptation in mobile networks by proposing a novel online learning algorithm called Learn2Adapt (L2A), which improves overall Quality of Experience and average streaming rate without requiring parameter tuning or channel model assumptions.
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.