This paper is concerned with the issue of improving video subscribers' quality of experience (QoE) by deploying a multi-unmanned aerial vehicle (UAV) network. Different from existing works, we characterize subscribers' QoE by video bitrates, latency, and frame freezing and propose to improve their QoE by energy-efficiently and dynamically optimizing the multi-UAV network in terms of serving UAV selection, UAV trajectory, and UAV transmit power. The dynamic multi-UAV network optimization problem is formulated as a challenging sequential-decision problem with the goal of maximizing subscribers' QoE while minimizing the total network power consumption, subject to some physical resource constraints. We propose a novel network optimization algorithm to solve this challenging problem, in which a Lyapunov technique is first explored to decompose the sequential-decision problem into several repeatedly optimized sub-problems to avoid the curse of dimensionality. To solve the sub-problems, iterative and approximate optimization mechanisms with provable performance guarantees are then developed. Finally, we design extensive simulations to verify the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can effectively improve the QoE of subscribers and is 66.75\% more energy-efficient than benchmarks.

翻译：本文关注通过部署多无人机网络来提升视频用户的体验质量。与现有研究不同，我们以视频比特率、延迟和帧冻结来表征用户的体验质量，并提出通过节能且动态地优化多无人机网络（包括服务无人机选择、无人机轨迹和无人机发射功率）来改善用户的体验质量。该动态多无人机网络优化问题被建模为一个具有挑战性的序列决策问题，目标是在物理资源约束下，最大化用户QoE的同时最小化网络总功耗。我们提出了一种新颖的网络优化算法来解决这一难题：首先利用Lyapunov技术将序列决策问题分解为若干重复优化的子问题以避免维数灾难；随后针对子问题，开发了具有可证明性能保证的迭代与近似优化机制；最后设计了大量仿真验证所提算法的有效性。仿真结果表明，该算法能够有效提升用户QoE，且比基准方法节能66.75%。