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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Joint resource optimization for nonorthogonal multiple access-enhanced scalable video coding multicast in unmanned aerial vehicle-assisted radio-access networks

  • Ziyuan Tong (College of Computer Science and Technology, Nanjing Tech University) ;
  • Hang Shen (College of Computer Science and Technology, Nanjing Tech University) ;
  • Ning Shi (Nanjing Trusted Blockchain and Algorithm Economics Institute) ;
  • Tianjing Wang (College of Computer Science and Technology, Nanjing Tech University) ;
  • Guangwei Bai (College of Computer Science and Technology, Nanjing Tech University)
  • Received : 2022.04.06
  • Accepted : 2022.06.23
  • Published : 2023.10.20
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Abstract

A joint resource-optimization scheme is investigated for nonorthogonal multiple access (NOMA)-enhanced scalable video coding (SVC) multicast in unmanned aerial vehicle (UAV)-assisted radio-access networks (RANs). This scheme allows a ground base station and UAVs to simultaneously multicast successive video layers in SVC with successive interference cancellation in NOMA. A video quality-maximization problem is formulated as a mixed-integer nonlinear programming problem to determine the UAV deployment and association, RAN spectrum allocation for multicast groups, and UAV transmit power. The optimization problem is decoupled into the UAV deployment-association, spectrum-partition, and UAV transmit-power-control subproblems. A heuristic strategy is designed to determine the UAV deployment and association patterns. An upgraded knapsack algorithm is developed to solve spectrum partition, followed by fast UAV power fine-tuning to further boost the performance. The simulation results confirm that the proposed scheme improves the average peak signal-to-noise ratio, aggregate videoreception rate, and spectrum utilization over various baselines.

Keywords

Acknowledgement

National Natural Science Foundation of China under Grants 61502230 and 61501224, the National Project Funding for Key R&D Programs under Grant 2018YFC0808500, the Natural Science Foundation of Jiangsu Province under Grant BK20201357, and the Six Talent Peaks Project in Jiangsu Province under Grant RJFW-020.

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