Horizontal and Vertical Collaboration for VR Delivery in MEC-Enabled Small-Cell Networks

TL;DR

This paper proposes a joint horizontal and vertical collaboration architecture in MEC-enabled small-cell networks to improve VR delivery by optimizing caching, power allocation, and task offloading, reducing latency.

Contribution

It introduces a novel collaboration architecture and a DBRB algorithm for joint resource management in MEC-enabled VR delivery networks.

Findings

The proposed architecture outperforms existing methods in reducing VR delivery latency.

The DBRB algorithm effectively solves the joint optimization problem.

Simulation results validate the advantages of the proposed approach.

Abstract

Due to the large bandwidth, low latency and computationally intensive features of virtual reality (VR) video applications, the current resource-constrained wireless and edge networks cannot meet the requirements of on-demand VR delivery. In this letter, we propose a joint horizontal and vertical collaboration architecture in mobile edge computing (MEC)-enabled small-cell networks for VR delivery. In the proposed architecture, multiple MEC servers can jointly provide VR head-mounted devices (HMDs) with edge caching and viewpoint computation services, while the computation tasks can also be performed at HMDs or on the cloud. Power allocation at base stations (BSs) is considered in coordination with horizontal collaboration (HC) and vertical collaboration (VC) of MEC servers to obtain lower end-to-end latency of VR delivery. A joint caching, power allocation and task offloading problem is then formulated, and a discrete branch-reduce-and-bound (DBRB) algorithm inspired by monotone optimization is proposed to effectively solve the problem. Simulation results demonstrate the advantage of the proposed architecture and algorithm in terms of existing ones.