Multi-Tier CloudVR: Leveraging Edge Computing in Remote Rendered Virtual Reality

Mehrabidavoodabadi, Abbas, Siekkinen, Matti, Kämäräinen, Teemu and Ylä-Jääski, Antti (2020) Multi-Tier CloudVR: Leveraging Edge Computing in Remote Rendered Virtual Reality. ACM Transactions on Multimedia Computing, Communications, and Applications. ISSN 1551-6857 (In Press)

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The availability of high bandwidth with low-latency communication in 5G mobile networks enables remote rendered real-time virtual reality (VR) applications. Remote rendering of VR graphics in a cloud removes the need for local personal computer (PC) for graphics rendering and augments weak graphics processing unit (GPU) capacity of standalone VR headsets. However, to prevent the added network latency of remote rendering from ruining user experience, rendering a locally navigable viewport that is larger than the field of view of the HMD is necessary. The size of the viewport required depends on latency: longer latency requires rendering a larger viewport and streaming more content. In this paper, we aim to utilize multi-access edge computing (MEC) to assist the backend cloud in such remote rendered interactive VR. Given the dependency between latency and amount and quality of the content streamed, our objective is to jointly optimize the trade-off between average video quality and delivery latency. Formulating the problem as mixed integer nonlinear programming (MINLP), we leverage the interpolation between client’s field of view (FoV) frame size and overall latency to convert the problem to integer nonlinear programming (INLP) model and then design efficient online algorithms to solve it. The results of our simulations supplemented by real-world user data reveal that enabling a desired balance between video quality and latency, our algorithm particularly achieves the improvements of on average about 22% and 12% in term of video delivery latency and 8% in term of video quality compared to respectively order-of-arrival, threshold-based and random-location strategies.

Item Type: Article
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 26 Nov 2020 10:09
Last Modified: 26 Nov 2020 11:02

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