Optimizing Adaptive Video Streaming in Mobile Networks via Online Learning
arXiv:1905.11705
Abstract
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.
9 pages, 3 figures, submitted to IEEE Transactions on Multimedia (under review)
References in corpus (5)
- Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale
- An Online Convex Optimization Approach to Dynamic Network Resource Allocation
- Online Convex Optimization with Time-Varying Constraints
- Online convex optimization and no-regret learning: Algorithms, guarantees and applications
- A Comparative Case Study of HTTP Adaptive Streaming Algorithms in Mobile Networks