Content-Sensing Based Resource Allocation for Delay-Sensitive VR Video Uploading in 5G H-CRAN

Abstract

Virtual reality (VR) is emerging as one of key applications in future fifth-generation (5G) networks. Uploading VR video in 5G network is expected to boom in near future, as general consumers could generate high-quality VR videos with portable 360-degree cameras and are willing to share with others. Heterogeneous networks integrating with 5G cloud-radio access networks (H-CRAN) provides high transmission rate for VR video uploading. To address the motion characteristic of UE (User Equipments) and small cell feature of 5G H-CRAN, in this paper we proposed a content-sensing based resource allocation scheme for delay-sensitive VR video uploading in 5G H-CRAN, in which the source coding rate of uploading VR video is determined by the centralized RA scheduling. This scheme jointly optimizes g-NB group resource allocation, RHH/g-NB association, sub-channel assignment, power allocation, and tile encoding rate assignment as formulated in a mixed-integer nonlinear problem (MINLP). To solve the problem, a three stage algorithm is proposed. Dynamic g-NB group resource allocation is first performed according to the UE density of each group. Then, joint RRH/g-NB association, sub-channel allocation and power allocation is performed by an iterative process. Finally, encoding tile rate is assigned to optimize the target objective by adopting convex optimization toolbox. The simulation results show that our proposed algorithm ensures the total utility of system under the constraint of maximum transmission delay and power, which also with low complexity and faster convergence.