Online Optimization of Video Capturing and Backhaul Scheduling for Multi-UAV Surveillance

The multi-Uncrewed Aerial Vehicle (UAV) networks have been widely studied due to their advantages in regional video surveillance. Specifically, an architecture of multi-UAV network is widely employed for seamless video capturing and backhaul transmission. The multi-UAV surveillance network can dynamically control its video capturing process and resolution selection, allowing for a trade-off between video quality and seamlessness, quantified by the Length of Inter-frame (LoI-F). Each UAV's video capturing, resolution selection, and backhaul scheduling are jointly optimized to maximize the network's average video quality while adhering to a maximum average LoI-F constraint per UAV, ensuring seamless video capturing. An online control algorithm leveraging the Lyapunov drift-plus-penalty method is proposed to address this joint online optimization problem. The network operations based on real-time UAV states are adapted online by the algorithm to enable efficient resource allocation. An optimality analysis of the algorithm is provided, demonstrating the effectiveness of it in achieving the desired goals. Furthermore, the proposed algorithm is extended to a two-timescale variant, specifically targeting the reduction of inter-frame jitter, a critical metric for video smoothness. The optimality loss of this two-timescale algorithm is proved to be upper bounded while preserving the asymptotic optimality of the online control algorithm. Extensive simulations validate the performance of our proposed online control algorithm, showcasing its ability to enhance average video quality compared to other schemes while enabling seamless video capturing for multi-UAV surveillance networks.