Learning from Experience: A Dynamic Closed-Loop QoE Optimization for Video Adaptation and Delivery

The quality of experience (QoE) is known to be subjective and context-dependent. Identifying and calculating the factors that affect QoE is indeed a difficult task. Recently, a lot of effort has been devoted to estimate the users QoE in order to improve video delivery. In the literature, most of the...

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Bibliographic Details
Published in:arXiv.org 2017-08
Main Authors: Triki, Imen, Zhu, Quanyan, Elazouzi, Rachid, Haddad, Majed, Xu, Zhiheng
Format: Article
Language:English
Subjects:
Optimization
User satisfaction
Online Access:Get full text
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Summary:The quality of experience (QoE) is known to be subjective and context-dependent. Identifying and calculating the factors that affect QoE is indeed a difficult task. Recently, a lot of effort has been devoted to estimate the users QoE in order to improve video delivery. In the literature, most of the QoE-driven optimization schemes that realize trade-offs among different quality metrics have been addressed under the assumption of homogenous populations. Nevertheless, people perceptions on a given video quality may not be the same, which makes the QoE optimization harder. This paper aims at taking a step further in order to address this limitation and meet users profiles. To do so, we propose a closed-loop control framework based on the users(subjective) feedbacks to learn the QoE function and optimize it at the same time. Our simulation results show that our system converges to a steady state, where the resulting QoE function noticeably improves the users feedbacks.
ISSN:2331-8422