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