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

作     者：Mehrabi, Abbas Siekkinen, Matti Kamarainen, Teemu Yla-Jaaski, Antti 

作者机构：Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NEI 8ST Tyne & Wear England Aalto Univ Dept Comp Sci Espoo 02150 Finland Univ Helsinki Dept Comp Sci Yliopistonkatu 4 Helsinki 00100 Finland 

出 版 物：《ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS》 (ACM Trans. Multimedia Comput. Commun. Appl.)

年 卷 期：2021年第17卷第2期

页      面：1–24页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Multi-access edge computing (MEC) rendered virtual reality (VR) joint optimization integer nonlinear programming (INLP) greedy algorithm 

摘      要：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 for graphics rendering and augments weak graphics processing unit capacity of stand-alone 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 article, we aim to utilize multi-access edge computing 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 tradeoff between average video quality and delivery latency. Formulating the problem as mixed integer nonlinear programming, we leverage the interpolation between client s field of view frame size and overall latency to convert the problem to integer nonlinear programming 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.