DL-assisted tradeoff design for multi-UAV real-time video streaming transmission

The requirements of multiple Unmanned Aerial Vehicle (UAV)-based video streaming transmission rapidly increase in flying ad-hoc networks (FANET). Due to diverse network features of FANET, tradeoff design in harsh networks for has become one of the research hotspots. The communication links between the nodes, however, are often unstable, especially in harsh network environments. This article presents a deep learning-based throughput predictor (DLTP) for promoting the Quality of Experience (QoE). Based on the DLTP, we propose an adaptive algorithm to achieve the tradeoff between the bandwidth, the load, and the video parameters based on the UAV flying status and Quality of Service (QoS) evaluation. Sufficient experimental results verify that compared with the existing methods such as FESTIVE and BOLA, our proposed DLTP achieved 38-76% improvement in latency reduction, 34-53% improvement in congestion control, 45-72% improvement in packet recovery, and 32-68% improvement in rebuffering efficiency.