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• W4290996813 abstract "The rapid development of multimedia and communication technology has resulted in an urgent need for high-quality video streaming. However, robust video streaming under fluctuating network conditions and heterogeneous client computing capabilities remains a challenge. In this paper, we consider an enhancement-enabled video streaming network under a time-varying wireless network and limited computation capacity. Enhancement means that the client can improve the quality of the downloaded video segments via image processing modules. We aim to design a joint bitrate adaptation and client-side enhancement algorithm toward maximizing the quality of experience (QoE). We formulate the problem as a Markov decision process (MDP) and propose a deep reinforcement learning (DRL)-based framework, named ENAVS. As video streaming quality is mainly affected by video compression, we demonstrate that the video enhancement algorithm outperforms the super-resolution algorithm in terms of signal-to-noise ratio and frames per second, suggesting a better solution for client processing in video streaming. Ultimately, we implement ENAVS and demonstrate extensive testbed results under real-world bandwidth traces and videos. The simulation shows that ENAVS is capable of delivering 5%&#x2212;14% more QoE under the same bandwidth and computing power conditions as conventional ABR streaming." @default.
• W4290996813 created "2022-08-13" @default.
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• W4290996813 date "2022-05-16" @default.
• W4290996813 modified "2023-10-18" @default.
• W4290996813 title "Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing" @default.
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• W4290996813 doi "https://doi.org/10.1109/icc45855.2022.9839262" @default.
• W4290996813 hasPublicationYear "2022" @default.
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