Edge-computing-assisted virtual reality computation offloading: an empirical study
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE Access
Abstract
Offloading heavy virtual reality (VR) computational operations to a network edge computation
entity is receiving increasing attention as a tool to wirelessly and energy efficiently provide low-end client
devices with high-quality and immersive interactive VR services anytime and anywhere across the globe.
In this work, we aim to provide an understanding of various characteristics of VR computation offloading
through comprehensive experiments conducted using a prototype testbed for edge-assisted VR processing
and streaming. First, we investigate the benefits of VR offloading in terms of computational load and power
consumption reduction for a client device compared to standalone operation. Next, we measure VR traffic
patterns, including frame size and data and packet rates with various settings, such as different resolution and
encoding options. We also measure several performance metrics associated with the quality of experience,
namely, frame rate, packet loss rate, and image quality, with various configuration settings. Then, we present
latency measurement studies and investigate per-component latency with various settings. Furthermore,
we report the rigorous experiments performed to study the impacts of latency and motion patterns on the
black borders formed due to image reprojection and the overfilling technique used to eliminate these black
borders.
Description
Journal Article
Keywords
Virtual reality, Edge computing, Offloading, VR streaming, Latency, Overfilling