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- W2592319314 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." @default.
- W2592319314 created "2017-03-16" @default.
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- W2592319314 date "2017-03-06" @default.
- W2592319314 modified "2023-09-27" @default.
- W2592319314 title "Learning from Experience: A Dynamic Closed-Loop QoE Optimization for Video Adaptation and Delivery" @default.
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- W2592319314 doi "https://doi.org/10.48550/arxiv.1703.01986" @default.
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