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.
Highlights
In recent years, virtual reality (VR) technology has been rapidly commercialized, forming a $209 billion market by 2022 as predicted in [1]
Note that the problem is hardly solved in one shot by the optimization toolbox due to the problem consists of multiple stages in the VR video uploading process (i.e., RHH/g-NB association, sub-channel allocation and power allocation, and tile encoding rate assignment), a three stage algorithm is proposed to solve the problem efficiently in this paper
Association (PAS) + Tile rate assignment with CVX (TRAC): The group resource allocation in stage 1 is fixed, and will not change when the User equipments (UEs) density in each group changed as the motion of UEs (i.e., VR video transmission requirement is different in each g-NB Group)
Summary
Virtual reality (VR) technology has been rapidly commercialized, forming a $209 billion market by 2022 as predicted in [1]. Inspired by the previous work of QoC [13], we propose a content-sensing RA scheme for VR video transmission over 5G H-CRAN, which aiming at joint VR video source coding and uplink RA optimization. Instead of considering max-SINR [17] based RA in H-CRAN, in which consists of RRH association, PA and sub-channel allocation (SA) [18], the proposed scheme optimizes the total QoC which defined as weighted function of tile source coding rate. Note that the problem is hardly solved in one shot by the optimization toolbox due to the problem consists of multiple stages in the VR video uploading process (i.e., RHH/g-NB association, sub-channel allocation and power allocation, and tile encoding rate assignment), a three stage algorithm is proposed to solve the problem efficiently in this paper.
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