Optimizing Adaptive Video Streaming in Mobile Networks via Online Learning

TL;DR

This paper introduces Learn2Adapt, an online learning-based algorithm for adaptive video streaming in mobile networks that enhances QoE without needing parameter tuning or channel assumptions.

Contribution

The paper presents a novel, parameter-free online learning algorithm for adaptive video streaming that is robust to fast channel variations in mobile networks.

Findings

Improves overall QoE in simulations

Increases average streaming rate

Effective across various channel and application scenarios

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.