Data CorrelationAware Resource Management in Wireless Virtual Reality (VR): An Echo State Transfer Learning Approach Providing seamless connectivity for wireless virtual reality (VR) users has emerged as a key challenge for future cloudenabled cellular networks. In this paper, the problem of wireless VR resource management is investigated for a wireless VR network in which VR contents are sent by a cloud to cellular small base stations (SBSs). The SBSs will collect tracking data from the VR users, over the uplink, in order to generate the VR content and transmit it to the endusers using downlink cellular links. For this model, the data requested or transmitted by the users can exhibit correlation, since the VR users may engage in the same immersive virtual environment with different locations and orientations. As such, the proposed resource management framework can factor in such spatial data correlation, so as to better manage uplink and downlink traffic. This potential spatial data correlation can be factored into the resource allocation problem to reduce the traffic load in both the uplink and downlink. In the downlink, the cloud can transmit 360xc2xb0 contents or specific visible contents (e.g., user field of view) that are extracted from the original 360xc2xb0 contents to the users according to the usersxe2x80x99 data correlation so as to reduce the backhaul traffic load. In the uplink, each SBS can associate with the users that have similar tracking information so as to reduce the tracking data size. This data correlationaware resource management problem is formulated as an optimization problem whose goal is to maximize the usersxe2x80x99 successful transmission probability, defined as the probability that the content transmission delay of each user satisfies an instantaneous VR delay target. To solve this problem, a machine learning algorithm that uses echo state networks (ESNs) with transfer learning is introduced. By smartly transferring information on the SBSxe2x80x99s utility, the proposed transferbased ESN algorithm can quickly cope with changes in the wireless networking environment due to usersxe2x80x99 content requests and content request distributions. Simulation results demonstrate that the developed algorithm achieves up to 15.8% and 29.4% gains in terms of successful transmission probability compared to Qlearning with data correlation and Qlearning without data correlation, respectively.

QoEDriven Resource Allocation Optimized for DelaySensitive VR Video Uploading over Cellular Network Uploading Virtual Reality (VR) video over cellular networks is expected to boom in near future, as general consumers could generate highquality VR videos with portable 360degree cameras and are willing to share with others. Consequently, concerns of uplink bandwidth and delay arose for current popular technology of tilebased VR video streaming, which requires high quality video to transcode into multiple representations for further adaptive streaming. Motivated by this, we proposed a novel scheme for uplink delivery of tilebased VR video over cellular network, in which encoding bit rate of each tile is determined by uplink resource allocation (RA), and quality of content (QoC) contribution of each tile and channel quality of user equipments (UEs) are jointly considered during RA. Moreover, the RA problem is formulated as a frequency and time dependent nondeterministic polynomial(NP)hard problem, which can be effectively solved by our proposed approximate convex algorithm. Simulation results show that the proposed algorithm can achieve higher utility, that is higher total quality of experience (QoE) for viewers.