A Reinforcement Learning Framework for Multi-source Adaptive Streaming

Autor:	Phuong Luu Vo, Quan M. Le, Ngoc Thanh Nguyen, Cuong T. Do, Thi Thanh Sang Nguyen, Nghia Thi Ai Nguyen
Rok vydání:	2021
Předmět:	Dynamic Adaptive Streaming over HTTP Computer science business.industry Dash Path (graph theory) Reinforcement learning Cache Enhanced Data Rates for GSM Evolution business Adaptation (computer science) Multi-source Computer network
Zdroj:	Computational Collective Intelligence ISBN: 9783030880804 ICCCI
Popis:	Dynamic adaptive streaming over HTTP (DASH) is widely used in video streaming recently. With DASH, a video is stored in multiple equal-playing-time chunks with different quality levels. Video chunks are in-order delivered from a single source over a path in traditional DASH. The adaptation function in video player chooses a suitable quality level to request depending on current network status for each video chunk. In modern networks such as content delivery networks, edge caching, content-centric networks, etc., popular video contents are replicated at multiple cache nodes. Utilizing multiple sources for video streaming is investigated in this paper. We propose a reinforcement learning based algorithm, called RAMS, for rate adaptation in multi-source video streaming. The proposed algorithm outperforms the other notable adaptation methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::695beaf74bfb1b1b516daaff4bc1ad61 https://doi.org/10.1007/978-3-030-88081-1_31 Zobrazit plný text záznamu