QoE Analysis in Cache-Enabled Multi-UAV Networks

This paper presents a quality of experience (QoE)-guided content delivery framework in cache-enabled multi-unmanned aerial vehicle (UAV) networks, measured from the content delay index (CDI), which is formulated based on the latency involved in the delivery of contents requested by ground-based mobile users. The mobile users use the cellular devices to raise the content requests. Additionally, multiple UAVs act as aerial base stations to deliver the requested contents to ground-based mobile users. The proposed work aims to maximize the QoE. Additionally, the existing works do not consider the necessity of creating groups of ground-based mobile users based on geographic locations in order to provide contents seamlessly. Therefore, we construct an optimization problem to maximize the average CDI, which is proved as an NP-hard problem. Further, multiple UAVs incur the challenges of placing the UAVs in 3D space over the distributed ground-based mobile users. To solve this NP-hard problem, we propose QoE analysis in cache-enabled multi-UAV networks (QMUN). It functions in three steps. First, it creates clusters for the ground-based mobile users using the K-means algorithm and finds the optimized 2D positions of all the UAVs. Second, it finds the optimized heights of all UAVs before assigning them to the individual cluster. Finally, it infuses proactive content caching and optimizes the cache policy. Therefore, the delay associated with the content delivery is minimized, which leads to the generation of high value of CDI. The results of simulation depict the usefulness of QMUN in terms of average CDI, average throughput, average end-to-end delay, and user satisfaction. Specifically, the results depict that the average CDI of each user improves by $11.04$%, $4.46$%, and $17.88$% compared to the QMUN Without Cache (QMUN-WC), Greedy, and Random Selection (RS).