CMU-VP: Cooperative Multicast and Unicast With Viewport Prediction for VR Video Streaming in 5G H-CRAN

Junchao Yang,Junxia Wang,Jiangtao Luo,Song Guo

doi:10.1109/access.2019.2941646

Abstract

Virtual reality (VR) is commonly regarded as one of 5G killer-applications. Transmission efficiency and quality of experience (QoE) are the most concerning issues for VR video streaming in 5G networks. Several multicast approaches were proposed to address these issues regardless of variance of personal viewports. In this paper, we explore a novel scheme combining multicast and unicast sessions in heterogeneous cloud-radio access networks (H-CRAN), in which a basic version of the video is transmitted to all users through the g-NB in a multicast session, and tiles of enhanced-version are transmitted to each viewer in a unicast session through its stationed remote radio head (RHH). To ensure the real-time content delivery, a user's viewport is predicted using a method based on historical trajectories and similarity of motion behavior, and then the tiles of predicted viewport in a version dependent on the channel quality are sent to the user in the unicast session. The scheme is formulated into a mixed-integer nonlinear problem (MINLP), and two near-optimal solutions are proposed to solve it by applying greedy approach and approximate approach, respectively. The simulation results show that our proposed scheme ensures better QoE under constrained bandwidth, and the proposed near-optimal solutions can efficiently solve the problem with low complexity and comparable performance.

Highlights

As the popularization of virtual reality (VR), increasing people are able to experience VR capabilities on affordable head-mounted display (HMDs) (e.g., HTC VIVE)
The quality of experience (QoE) can be guaranteed by unicast but it will result in ineffective transmission, especially when considerable number of people are request same VR content simultaneously. Is it possible to transmit VR video by taking advantage of multicast for transmission efficiency and unicast for QoE improvement ? Motivated by this idea, in this paper, we explore VR video streaming in 5G heterogeneous cloud-radio access networks (H-CRAN) through cooperative multicast and unicast (CMU) by macro (i.e., g-NB) and small cell (i.e., RRH), respectively
In order to achieve better transmission efficiency and QoE improvement, we proposed the cooperative multicast and unicast with viewport prediction (CMU-VP) scheme for VR video streaming in H-CRAN

Summary

INTRODUCTION

As the popularization of virtual reality (VR), increasing people are able to experience VR capabilities on affordable head-mounted display (HMDs) (e.g., HTC VIVE). In order to achieve better transmission efficiency and QoE improvement, we proposed the cooperative multicast and unicast with viewport prediction (CMU-VP) scheme for VR video streaming in H-CRAN. An efficient User-Generated Content (UGC) VR video transmission scheme over cellular network is represented in our previous work [25], in which only one representation of each tile is generated for uploading based on optimization of uplink resource allocation under the consideration of quality of content (QoC) contribution, directly transmitted to viewer without transcoding. Athul Prasad et al [18] discussed the challenges for VR broadcast using 5G small cell network, solutions in terms of usage of single frequency network (SFN) type of deployments and unlicensed millimeter wave (mmW) bands were considered They proposed a D2D assisted VR broadcast scheme that enabled radio resource efficient delivery VR video using broadcast transmission [19]. Viewing probability of each tile pt for the user can be written as (4), i.e.,normalizing votes for each tile

PROPOSED CMU-VP SCHEME

SIMULATIONS

Findings

CONCLUSION