Performance Study of Mixed Reality for Edge Computing

Abstract

Edge computing is a recent paradigm where computing resources are placed close to the user, at the edge of the network. This is a promising enabler for applications that are too resource-intensive to be run on an end device, but at the same time require too low latency to be run in a cloud, such as for example mixed reality (MR). In this work, we present MR-Leo, a prototype for creating an MR-enhanced video stream. It enables offloading of the point cloud creation and graphic rendering at the edge. We study the performance of the prototype with regards to latency and throughput in five different configurations with different alternatives for the transport protocol, the video compression format and the end/edge devices used. The evaluations show that UDP and MJPEG are good candidates for achieving acceptable latency and that the design of the communication protocol is critical for offloading video stream analysis to the edge.