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Delay-constrained quality maximization in RSMA-based video streaming networks

Authors
Tran, Anh-TienLakew, Demeke ShumeyeTran, Nam-PhuongDao, Nhu-NgocCho, Sungrae
Issue Date
Oct-2022
Publisher
Association for Computing Machinery
Keywords
bitrate adaptation; C-RAN; deep reinforcement learning; rate-splitting multiple access; video streaming
Citation
Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), pp 276 - 280
Pages
5
Journal Title
Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
Start Page
276
End Page
280
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59064
DOI
10.1145/3492866.3558589
ISSN
0000-0000
Abstract
Recent studies have shown that rate splitting multiple access (RSMA), which depends on multi-antenna rate splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, successfully controls interference in multi-antenna communication networks. This paper examines RSMA's applicability to video streaming applications in cloud radio access networks (C-RAN). We aim to address a practical challenge to maximize the perceived quality of end users while keeping the delay constraints remained satisfied using RSMA. We propose a learning-based framework to select appropriate video quality together with beamforming vectors according to current defined system state. The simulation figure confirms that the learning behavior of proposed learning scheme is stable. © 2022 ACM.
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소프트웨어대학 (소프트웨어학부)
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