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GLOBECOM 2012, Anaheim : États-Unis (2012)
Online Learning for QoE-based Video Streaming to Mobile Receivers
Nesrine Changuel 1, Bessem Sayadi 1, Michel Kieffer 2, 3, 4
(2012-12-07)

This paper proposes a cross-layer control mechanism to stream efficiently scalable videos to mobile receivers. Its goal is to maximize the quality of the received video while accounting for the variations of the characteristics of the transmitted content and of the channel. The control problem is cast in the framework of Markov Decision Processes. The optimal actions to apply to the system are learned using reinforcement learning. For that purpose, the quality of the decoded frames at receiver is inferred by an observation (i) of the quality of the various scalability layers and (ii) of the level of queues at the Application and Medium Access Control layers of the transmitter only. Delayed as well as absence of information on the channel state are considered. Experiments show that the performance of the proposed solution is only slightly degraded with delayed or missing channel state information. The performance degradation is larger when considering a basic bitstream extractor, which serves as reference.
1:  Alcatel Lucent Bell Labs
ALCATEL
2:  Laboratoire des signaux et systèmes (L2S)
UMR8506 CNRS – SUPELEC – Université Paris XI - Paris Sud
3:  Laboratoire Traitement et Communication de l'Information [Paris] (LTCI)
Télécom ParisTech – CNRS : UMR5141
4:  Institut Universitaire de France (IUF)
Ministère de l'Enseignement Supérieur et de la Recherche Scientifique
Division Télécoms et Réseaux
Computer Science/Networking and Telecommunication

Computer Science/Multimedia
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