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- W4387664188 abstract "Under the Covid-19 epidemic’s influence, academic events must be conducted online through live broadcasts. Therefore, improving the live user quality experience is vital for academic activities. However, in most live streaming, users suffer from low quality, frequent buffering, and long delays. ABR algorithm is one of the economic methods to solve the above problems. This paper models the ABR problem through mathematical formalization and designs an ABR solution and framework called RL-LIVE based on reinforcement learning. Secondly, we published the training data set with features of academic activities and the platform required for training. Finally, after evaluation, the RL-based ABR algorithm has a performance improvement of 7.5%-23% in various scenarios." @default.
- W4387664188 created "2023-10-17" @default.
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- W4387664188 date "2023-10-17" @default.
- W4387664188 modified "2023-10-17" @default.
- W4387664188 title "RL-based adaptive live video streaming for academic event" @default.
- W4387664188 doi "https://doi.org/10.1117/12.3009563" @default.
- W4387664188 hasPublicationYear "2023" @default.
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