(A) multi-objective framework for dynamic QoE across users in video streaming = 비디오 스트리밍에서의 다중 및 동적 사용자 QoE를 위한 다목적 프레임워크

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Optimizing for user quality of experience (QoE) is a prevailing approach to improving Internet video streaming. The effectiveness of adaptive bitrate (ABR) algorithms is often measured by a weighted combination of conflicting objectives, such as video quality, delay, and smoothness. We highlight the subjectivity of QoE reward formulation, which prevails, but results in various and discrete QoE metrics that assume a correct knowledge of user preferences. We propose MARVEL (A multi-objective approach to reinforcement “video experience” learning), a framework for reformulating QoE as a generalized multi-objective problem with dynamic weights, discarding the assumption of prior knowledge of user preferences. We employ a multi-objective reinforcement learning (MORL) based module within the framework, demonstrating an applied solution that adapts to dynamic user preferences (both across users and within the same session). In an experimental trial with randomized across possible user preferences weights, we show MARVEL learned the frontier solutions of different preferences on the two-dimensional metric. MARVEL outperforms previous RL-based approaches to ABR by 16% ∼ 39% in both adaptations and single objective analysis.
Advisors
Yi, Yungresearcher이융researcherHan, Dongsuresearcher한동수researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2020.8,[ii, 20 p. :]

Keywords

Video streaming QoE▼amulti-objective optimization▼auser preferences adaptation▼areinforcement learning; 비디오 스트리밍 QoE▼a멀티벤더 최적화▼a사용자 기본 설정 적응▼a강화 학습

URI
http://hdl.handle.net/10203/285085
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=925249&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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