Online Learning for QoE-based Video Streaming to Mobile Receivers

Abstract : 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.
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Nesrine Changuel, Bessem Sayadi, Michel Kieffer. Online Learning for QoE-based Video Streaming to Mobile Receivers. GLOBECOM 2012, Dec 2012, Anaheim, United States. pp.1-5. ⟨hal-00727798⟩

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