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In this paper, the problem of content caching and transmission is studied fora wireless virtual reality (VR) network in which unmanned aerial vehicles(UAVs) capture videos on live games or sceneries and transmit them to smallbase stations (SBSs) that service the VR users. However, due to its limitedcapacity, the wireless network may not be able to meet the delay requirementsof such 360 content transmissions. To meet the VR delay requirements, the UAVscan extract specific visible content (e.g., user field of view) from theoriginal 360 data and send this visible content to the users so as to reducethe traffic load over backhaul and radio access links. To further alleviate theUAV-SBS backhaul traffic, the SBSs can also cache the popular contents thatusers request. This joint content caching and transmission problem isformulated as an optimization problem whose goal is to maximize the users'reliability, defined as the probability that the content transmission delay ofeach user satisfies the instantaneous VR delay target. To address this problem,a distributed deep learning algorithm that brings together new neural networkideas from liquid state machine (LSM) and echo state networks (ESNs) isproposed. The proposed algorithm enables each SBS to predict the users'reliability so as to find the optimal contents to cache and contenttransmission format for each UAV. Analytical results are derived to expose thevarious network factors that impact content caching and content transmissionformat selection. Simulation results show that the proposed algorithm yields25.4% gain of reliability compared to Q-learning. The results also show thatthe proposed algorithm can achieve 14.7% gain of reliability due to thereduction of traffic load over backhaul compared to the proposed algorithm withrandom caching.

Echo-Liquid State Deep Learning for $360^\circ$ Content Transmission and Caching in Wireless VR Networks with Cellular-Connected UAVs