QoE-Driven Video Transmission: Energy-Efficient Multi-UAV Network Optimization

TL;DR

This paper introduces an energy-efficient multi-UAV network optimization method that dynamically enhances video streaming quality of experience (QoE) by optimizing UAV deployment, trajectory, and power, outperforming benchmarks significantly.

Contribution

It presents a novel Lyapunov-based optimization algorithm for dynamic multi-UAV network management, improving QoE and energy efficiency over existing methods.

Findings

QoE improved significantly in simulations

Energy consumption reduced by 66.75% compared to benchmarks

Effective optimization of UAV trajectories and power levels

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. 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.