Communications, Caching, and Computing for Mobile Virtual Reality: Modeling and Tradeoff

Abstract

Virtual reality (VR) over wireless is emerging as an important use case of 5G networks. Immersive VR experience requires the delivery of huge data at ultra-low latency, thus demanding ultra-high transmission rate. This challenge can be largely addressed by the recent network architecture known as mobile edge computing (MEC), which enables caching and computing capabilities at the edge of wireless networks. This paper presents a novel MEC-based mobile VR delivery framework that is able to cache parts of the field of views (FOVs) in advance and run certain post-processing procedures at the mobile VR device. To optimize resource allocation at the mobile VR device, we formulate a joint caching and computing decision problem to minimize the average required transmission rate while meeting a given latency constraint. When FOVs are homogeneous, we obtain a closed-form expression for the optimal joint policy which reveals interesting communicationscaching-computing tradeoffs. When FOVs are heterogeneous, we obtain a local optima of the problem by transforming it into a linearly constrained indefinite quadratic problem then applying concave convex procedure. Numerical results demonstrate great promises of the proposed mobile VR delivery framework in saving communication bandwidth while meeting low latency requirement.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

“…The newly emerging applications corresponding to mobile AR, VR, and wearable devices, e.g., smart glasses and watches, are anticipated to be among the most demanding applications over wireless networks so far, but there is still lack of sufficient capacities to execute sophisticated data processing algorithms. To overcome such challenges, the emergence of MEC and 5G techniques would pose the longer battery lifetime, powerful set of computing and storage resources, and low end-to-end latency [163], [194]. Sharing this view, [161] presented Outlet system to explore the available computing resources from users' ambiance, e.g., from nearby smart phones, tablets, computers, Wi-Fi APs, to form an MEC platform for executing the offloading tasks from wearable devices.…”

“…This work also demonstrated the interesting tradeoffs among communications, computing, and caching. In [163], a novel delivery framework enabling field of views caching and post-processing procedures at the mobile VR device was proposed to save communication bandwidth while meeting low latency requirement. Impressively, an implementation of MEC concepts over Android OS and Unity VR application engine in [164] enabled to reduce more than 90% computation burden and more than 95% of the VR frame data.…”

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

“…As the proposed problem is NP-hard, the problem is solved using a genetic algorithm. Next, Sun et al [252] study the joint allocation of resources for mobile VR that includes both 3D and 2D content. They analyze the different trade-offs between utilizing both computing and caching resources for delivering VR streams that contain 3D content.…”

Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems

“…They apply a novel deep reinforcement learning approach to optimally allocating the network resources. Sun et al [33] presented a MEC-based mobile VR delivery framework to minimize the average required transmission rate. The main concern of task offloading strategy in their work is what, how and where to offload UEs' tasks with the current network conditions.…”