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.
@article{arxiv.1905.11705,
title = {Optimizing Adaptive Video Streaming in Mobile Networks via Online Learning},
author = {Theodoros Karagkioules and Georgios S. Paschos and Nikolaos Liakopoulos and Attilio Fiandrotti and Dimitrios Tsilimantos and Marco Cagnazzo},
journal= {arXiv preprint arXiv:1905.11705},
year = {2019}
}
Comments
9 pages, 3 figures, submitted to IEEE Transactions on Multimedia (under review)