Abstract
In this paper, we propose a cross-layer decision framework for multiuser adaptive video delivery over time-varying and mutually interfering wireless cellular network. The key idea is to synthetically design the physical-layer optimization-based beamforming scheme (performed at the base stations) and the application-layer deep reinforcement learning (DRL)-based rate adaptation scheme (performed at the user terminals), so that a very complex multi-user overall fair long-Term quality of experience (QoE) maximization problem can be decomposed to two layers and solved effectively. Extensive simulations show that the proposed cross-layer design is effective and promising.
Original language | English (US) |
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Title of host publication | 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728137230 |
DOIs | |
State | Published - Dec 2019 |
Event | 34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019 - Sydney, Australia Duration: Dec 1 2019 → Dec 4 2019 |
Publication series
Name | 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019 |
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Conference
Conference | 34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019 |
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Country/Territory | Australia |
City | Sydney |
Period | 12/1/19 → 12/4/19 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT This work was supported in part by the National Natural Science Foundation of China under No. 61871267, Grant 61831018, Grant 61529101, Grant 61622112, No. 61931023 and No. 61972256, NSF grants CMMI-1727757 and CCF-1526078, and the China Scholarship Council.
Publisher Copyright:
© 2019 IEEE.
Keywords
- Wireless video streaming
- beamforming
- cross-layer design
- deep reinforcement learning
- rate adaptation