QoE-Driven UAV Video Transmission in a Multi-UAV Network

Kesong Wu, Pengbin Yang, Xianbin Cao, Zongyang Yu
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引用次数: 1

Abstract

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. Simulation results show that the proposed algorithm can effectively improve the QoE of subscribers and is 45.69% more energy-efficient than benchmarks.
多无人机网络中qos驱动的无人机视频传输
本文研究了通过部署多无人机网络来提高视频用户体验质量的问题。与已有研究不同的是,本文通过视频比特率、延迟和帧冻结来表征用户的QoE,并提出从服务无人机选择、无人机轨迹和无人机发射功率等方面,通过高能效和动态优化多无人机网络来提高用户的QoE。动态多无人机网络优化问题是一个具有挑战性的顺序决策问题,其目标是在一定的物理资源约束下,使用户的QoE最大化,同时使网络总功耗最小。我们提出了一种新的网络优化算法来解决这一具有挑战性的问题,该算法首先探索了Lyapunov技术,将顺序决策问题分解为几个重复优化的子问题,以避免维数的诅咒。为了求解子问题,提出了具有可证明性能保证的迭代和近似优化机制。仿真结果表明,该算法能有效提高用户的QoE,比基准测试节能45.69%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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