Paper on optimized Cloud VR traffic service accepted for IEEE ACCESS
The paper “Adaptive Cloud-Based Extended Reality: Modeling and Optimization” by Mikhail Liubogoshchev, Kamila Ragimova, Andrey Lyakhov, Siyu Tang, and Evgeny Khorov has been accepted for publication in IEEE Access. A link to the paper will be available soon.
The paper studies the joint service of the adaptive Cloud XR traffic with other high-priority delay-sensitive traffics. Extended Reality (XR), which includes Virtual Reality, Augmented Reality, and Mixed Reality, promises to bring the virtual and telepresence experience to another level. To make XR more affordable, in Cloud XR, the headsets just capture the user’s actions and wirelessly send the data to some remote server. The server renders the virtual XR scene according to the received data, encodes it into a video stream, and sends it back to the headset that shows the video to the user. Such a system imposes strict requirements on data transmission reliability, bandwidth, and delays. The satisfaction of these requirements becomes an extremely challenging problem in the presence of other types of delay-sensitive traffic, such as control commands of the Cloud XR application itself.
In a desire to solve the emerging problem, the authors, at first, have designed a novel mathematical model of a real-time adaptive Cloud XR application. The model allows evaluating Quality of Experience (QoE) for Cloud XR users in various scenarios. Contrary to the other ones, it takes into account the variation in the network delivery rate caused by random interference from high-priority traffic. Using the model, the paper estimates the network capacity for the Cloud XR traffic and optimizes the bitrate adaptation function of the Cloud XR video streaming application. The goal of the optimization is to improve the visual quality of the virtual environment observed by the users, subject to the constrained probability of image impairments due to excessive delivery delays.
With simulations, the authors demonstrate the high accuracy of the model and show that the optimal bitrate adaptation function can provide up to 2 times higher average bitrate than one of the state-of-the-art while keeping the stalling probability below the required constraint. The article is published in IEEE Access, a first quartile (Q1) journal. This open multidisciplinary journal has won numerous awards and its articles are in IEEE Xplore’s Most Popular downloads every month. IEEE Access is an open-access journal, meaning that anyone can freely familiarize themselves with the articles published in it.