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• W1998431331 abstract "HTTP Adaptive Streaming (HAS) is becoming the de facto standard for Over-The-Top (OTT)-based video streaming services such as YouTube and Netflix. By splitting a video into multiple segments of a couple of seconds and encoding each of these at multiple quality levels, HAS allows a video client to dynamically adapt the requested quality during the playout to react to network changes. However, state-of-the-art quality selection heuristics are deterministic and tailored to specific network configurations. Therefore, they are unable to cope with a vast range of highly dynamic network settings. In this letter, a novel Reinforcement Learning (RL)-based HAS client is presented and evaluated. The self-learning HAS client dynamically adapts its behaviour by interacting with the environment to optimize the Quality of Experience (QoE), the quality as perceived by the end-user. The proposed client has been thoroughly evaluated using a network-based simulator and is shown to outperform traditional HAS clients by up to 13% in a mobile network environment." @default.
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• W1998431331 date "2014-04-01" @default.
• W1998431331 modified "2023-10-01" @default.
• W1998431331 title "Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client" @default.
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• W1998431331 doi "https://doi.org/10.1109/lcomm.2014.020414.132649" @default.
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