SMASH: a Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP.

Loading...
Thumbnail Image
Files
Data_centric_Approach.pdf(440.86 KB)
Accepted version
Date
2020-05
Authors
Sani, Yusuf
Raca, Darijo
Quinlan, Jason J.
Sreenan, Cormac J.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Research Projects
Organizational Units
Journal Issue
Abstract
The growth of online video-on-demand consumption continues unabated. Existing heuristic-based adaptive bitrate (ABR) selection algorithms are typically designed to optimise video quality within a very narrow context. This may lead to video streaming providers implementing different ABR algorithms/players, based on a network connection, device capabilities, video content, etc., in order to serve the multitude of their usersâ streaming requirements. In this paper, we present SMASH: a Supervised Machine learning approach to Adaptive Streaming over HTTP, which takes a tentative step towards the goal of a one-size-fits-all approach to ABR. We utilise the streaming output from the adaptation logic of nine ABR algorithms across a variety of streaming scenarios (generating nearly one million records) and design a machine learning model, using systematically selected features, to predict the optimal choice of the bitrate of the next video segment to download. Our evaluation results show that not only does SMASH guarantee a high QoE but its performance is consistent across a variety of streaming contexts.
Description
Keywords
SMASH , HAS , HTTP Adaptive Streaming, , DASH , Machine Learning
Citation
Sani, Y., Raca, D., Quinlan, J. J. and Sreenan, C. J. (2020) 'SMASH: A Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP', 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), 26-28 May 2020, 1-6, doi: 10.1109/QoMEX48832.2020.9123139
Copyright
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.