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Online learning adaptation strategy for DASH clients

Chiariotti, F; D'Aronco, S; Toni, L; Frossard, P; (2016) Online learning adaptation strategy for DASH clients. In: Proceedings of the 7th International Conference on Multimedia Systems. ACM (Association for Computing Machinery): New York, NY, USA. Green open access

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Abstract

In this work, we propose an online adaptation logic for Dynamic Adaptive Streaming over HTTP (DASH) clients, where each client selects the representation that maximize the long term expected reward. The latter is defined as a combination of the decoded quality, the quality fluctuations and the rebuffering events experienced by the user during the playback. To solve this problem, we cast a Markov Decision Process (MDP) optimization for the selection of the optimal representations. System dynamics required in the MDP model are a priori unknown and are therefore learned through a Reinforcement Learning (RL) technique. The developed learning process exploits a parallel learning technique that improves the learning rate and limits sub-optimal choices, leading to a fast and yet accurate learning process that quickly converges to high and stable rewards. Therefore, the efficiency of our controller is not sacrificed for fast convergence. Simulation results show that our algorithm achieves a higher QoE than existing RL algorithms in the literature as well as heuristic solutions, as it is able to increase average QoE and reduce quality fluctuations.

Type: Proceedings paper
Title: Online learning adaptation strategy for DASH clients
Event: MMSys '16 - 7th ACM International Conference on Multimedia Systems
Location: Klagenfurt, Austria
Dates: 10 May 2016 - 13 May 2016
ISBN-13: 9781450342971
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/2910017.2910603
Publisher version: http://doi.org/10.1145/2910017.2910603
Language: English
Additional information: © ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in: Proceedings of the 7th International Conference on Multimedia Systems, http://doi.org/10.1145/2910017.2910603
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1533070
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