Utility-Oriented Optimization for Video Streaming in UAV-Aided MEC Network: A DRL Approach

• Published in: IEEE transactions on green communications and networking Vol. 8; no. 2; pp. 878 - 889
• Main Authors: Miao, Jiansong, Bai, Shanling, Mumtaz, Shahid, Zhang, Qian, Mu, Junsheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous aerial vehicles; Communication networks; Convexity; DDPG; Edge computing; Energy consumption; Ground stations; Heuristic algorithms; Machine learning; Markov processes; Military applications; Mobile computing; mobile edge computing; Multi-access edge computing; Network latency; Optimization; Resource allocation; Resource management; Streaming media; Trajectory; Transcoding; UAV; Unmanned aerial vehicles; utility; Video coding; video transcoding; Video transmission
• ISSN: 2473-2400; 2473-2400
• DOI: 10.1109/TGCN.2024.3352173

The integration of unmanned aerial vehicles (UAVs) in future communication networks has received great attention, and it plays an essential role in many applications, such as military reconnaissance, fire monitoring, etc. In this paper, we consider a UAV-aided video transmission system based on mobile edge computing (MEC). Considering the short latency requirements, the UAV acts as a MEC server to transcode the videos and as a relay to forward the transcoded videos to the ground base station. Subject to constraints on discrete variables and short latency, we aim to maximize the cumulative utility by jointly optimizing the power allocation, video transcoding policy, computational resources allocation, and UAV flight trajectory. The above non-convex optimization problem is modeled as a Markov decision process (MDP) and solved by a deep deterministic policy gradient (DDPG) algorithm to realize continuous action control by policy iteration. Simulation results show that the DDPG algorithm performs better than deep Q-learning network algorithm (DQN) and actor-critic (AC) algorithm.