Energy Efficiency Maximization for Secure Live Video Streaming in UAV Wireless Networks

Unmanned aerial vehicles (UAVs) have shown great potential in live video streaming applications, especially in surveillance and reconnaissance. However, ensuring high quality of service (QoS) remains a challenge due to the dynamic nature of wireless channels. In this paper, we tackle the crucial challenge of energy-efficient and secure UAV-enabled live video streaming. To maximize long-term energy efficiency, we propose a cross-layer optimization framework that coordinates the adjustment of video coding parameters, wireless resource allocation, and UAV trajectory planning. We formulate the joint optimization as a constrained Markov decision process (CMDP) to capture the complex interdependencies between video quality, energy usage, and security risks. We introduce a new performance metric that captures the trade-off between video quality and energy consumption. The core of our method is a customized first-order constrained policy optimization, which efficiently handle complex real-world constraints like UAV battery capacities and end-to-end transmission delays. Our approach achieves scalability and sample efficiency with minimal gradient information. Through extensive system modeling and simulations under various network conditions, we validate the effectiveness of the proposed method compared with existing reinforcement learning algorithms.

I. Introduction

Unmanned aerial vehicles (UAVs) have great potential for live video streaming applications [1], [2]. UAVs efficiently capture and transmit real-time visual data using high-definition cameras. However, ensuring smooth and reliable video streaming remains challenging due to dynamic wireless channels and limited on-board resources [1], [3], [4].