Offloading Revenue Maximization in Multi-UAV-Assisted Mobile Edge Computing for Video Stream
Bin Li, Huimin Shan · IEEE Internet of Things Journal · 2024
Traditional video transmission systems assisted by multiple uncrewed aerial vehicles (UAVs) are often limited by computing resources, making it challenging to meet the demands for efficient video processing. To solve this challenge, this article presents a multi-UAV-assisted device-to-device (D2D) mobile edge computing system for the maximization of task offloading profits in video stream transmission. In particular, the system enables UAVs to collaborate with idle user devices to process video computing tasks by introducing D2D communications. To maximize the system efficiency, the article jointly optimizes power allocation, video transcoding strategies, computing resource allocation, and UAV trajectory. The resulting nonconvex optimization problem is formulated as a Markov decision process (MDP) and solved relying on the twin delayed deep deterministic policy gradient (TD3) algorithm. Numerical results indicate that the proposed TD3 algorithm performs a significant advantage over other traditional algorithms in enhancing the overall system efficiency.