Learning from experience: A dynamic closed-loop QoE optimization for video adaptation and delivery
Open accessAbstract
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 a hard task. This paper aims at taking a step further in order to address this limitation and meet users' profiles. Specifically, 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. Extensive simulation results show that the proposed scheme converges to a steady state, where the resulting QoE function noticeably improves the users' feedbacks.
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Self-citationAnticipating Resource Management and QoE for Mobile Video Streaming under Imperfect Prediction
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QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
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- Authors
- Triki, I., El-Azouzi, R., Haddad, M., Zhu, Q., & Xu, Z.
- Affiliations
- Université d'Avignon et des Pays de Vaucluse (FR)
- New York University (US)
- Venue
- 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1-5
- OpenAlex
- W2963081970
- Cited by
- 11
- References
- 14
- Citations by year
- 2024: 1 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 3 · 2018: 1 · 2017: 1
- FWCI
- 0.56
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1720230, 1544782, 1541164)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Keywords
- quality of experience, video adaptation, video delivery, feedback control, user feedback, user heterogeneity
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