Learning from Experience: A Dynamic Closed-Loop QoE Optimization for Video Adaptation and Delivery
This addresses the problem of personalized video delivery for users by moving beyond homogeneous population assumptions, though it appears incremental as it builds on existing QoE optimization efforts.
The paper tackles the challenge of optimizing video quality of experience (QoE) by accounting for individual user differences, proposing a closed-loop control framework that learns and optimizes QoE based on subjective feedback, with simulation results showing convergence to a steady state that improves user feedback.
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.