Energy-Efficient Secure Video Streaming in UAV-Enabled Wireless Networks: A Safe-DQN Approach

Abstract

Unmanned aerial vehicles (UAVs) are anticipated to be integrated into the next generation wireless networks as new aerial mobile nodes, which can provide various live streaming applications such as surveillance, reconnaissance, etc. For such applications, due to the dynamic characteristics of traffic and wireless channels, how to guarantee the quality of service (QoS) is a challenging task. In this paper, with recent advances in scalable video coding (SVC), we study energy-efficient secure video streaming in rotary-wing UAV-enabled wireless networks. By jointly optimizing video levels selection, power allocation, and the UAV’s trajectory, we intend to maximize the long-term energy efficiency that is defined as the ratio of video quality to power consumption. Meanwhile, secrecy timeout probability is considered as a constraint cost to guarantee time delays requirements in a long run perspective. Our problem is modeled as a constrained Markov decision process (CMDP) and solved by safe deep Q-learning network (safe-DQN), where a safe policies set induced by constructing a Lyapunov function is dynamically adjusted to satisfy the constraint conditions of the CMDP. Extensive simulation results with different system parameters show the effectiveness of the proposed algorithm compared with other existing reinforcement learning algorithms.