QoE Optimization for Live Video Streaming in UAV-to-UAV Communications via Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning approach to optimize UAV movements and transmission parameters for live video streaming in UAV-to-UAV communications, enhancing QoE during wildfire rescue operations.

Contribution

It introduces a joint optimization framework using DQN and Actor-Critic methods for real-time UAV movement, power control, and video resolution to improve streaming QoE.

Findings

The proposed algorithm outperforms greedy algorithms in QoE metrics.

Significant reduction in delay and improved video smoothness.

Effective adaptation to dynamic fire and wireless channel conditions.

Abstract

A challenge for rescue teams when fighting against wildfire in remote areas is the lack of information, such as the size and images of fire areas. As such, live streaming from Unmanned Aerial Vehicles (UAVs), capturing videos of dynamic fire areas, is crucial for firefighter commanders in any location to monitor the fire situation with quick response. The 5G network is a promising wireless technology to support such scenarios. In this paper, we consider a UAV-to-UAV (U2U) communication scenario, where a UAV at a high altitude acts as a mobile base station (UAV-BS) to stream videos from other flying UAV-users (UAV-UEs) through the uplink. Due to the mobility of the UAV-BS and UAV-UEs, it is important to determine the optimal movements and transmission powers for UAV-BSs and UAV-UEs in real-time, so as to maximize the data rate of video transmission with smoothness and low latency, while mitigating the interference according to the dynamics in fire areas and wireless channel conditions. In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming. To learn the Deep Q-Network (DQN) and Actor-Critic (AC) to maximize the QoE of video transmission from all UAV-UEs to a single UAVBS. Simulation results show the effectiveness of our proposed algorithm in terms of the QoE, delay and video smoothness as compared to the Greedy algorithm.