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- W1915476907 abstract "At present, HTTP Adaptive Streaming (HAS) is developing into a key technology for video delivery over the Internet. In this delivery strategy, the client proactively and adaptively requests a quality version of chunked video segments based on its playback buffer, the perceived network bandwidth and other relevant factors. In this paper, we discuss the use of reinforcement-learning (RL) to learn the optimal request strategy at the HAS client by progressively maximizing a pre-defined Quality of Experience (QoE)-related reward function. Under the framework of RL, we investigate the most influential factors for the request strategy, using a forward variable selection algorithm. The performance of the RL-based HAS client is evaluated by a Video-on-Demand (VOD) simulation system. Results show that given the QoE-related reward function, the RL-based HAS client is able to optimize the quantitative QoE. Comparing with a conventional HAS system, the RL-based HAS client is more robust and flexible under versatile network conditions." @default.
- W1915476907 created "2016-06-24" @default.
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- W1915476907 date "2014-01-01" @default.
- W1915476907 modified "2023-09-25" @default.
- W1915476907 title "Factor Selection for Reinforcement Learning in HTTP Adaptive Streaming" @default.
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- W1915476907 doi "https://doi.org/10.1007/978-3-319-04114-8_47" @default.
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