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Offloading virtual reality (VR) computations to a cloud computing entity can enable support for VR services on low-end user devices but may result in increased latency, which will lead to mismatch between the user’s viewport and the received VR image, thus inducing motion sickness. Predicting future motion and rendering future images accordingly is a promising solution to the latency problem. In this paper, we develop velocity- and error-aware model switching schemes applicable to a wide range of existing motion prediction models. First, we consider the chattering problem of machine learning (ML)-based prediction models and the relationship between the velocity and the prediction error gap between an ML model and the case of no prediction (NOP). Accordingly, we propose a velocity-aware switching (VAS) scheme that combines the outputs from the ML model and the NOP case via a weight determined by the head motion velocity. Next, we develop an ensemble method combining a set of outputs from VAS and other models, called error-aware switching (EAS). EAS switches between model outputs based on the error statistics of those outputs under the parallel execution of multiple models, including VAS models. For EAS, schemes for both hard switching and soft integration of the model outputs are proposed. We evaluate the proposed schemes based on real VR motion traces for diverse ML-based prediction models.

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.