Towards strong continuous consistency in edge-assisted VR-SGs: Delay-differences sensitive online task redistribution

Abstract

Virtual Reality Serious Games (VR-SGs) integrate immersive virtual reality (VR) technology with instruction-oriented serious games (SGs), aiming to improve the efficiency of educational and training programs. VR-SG’s training effectiveness is highly contingent upon the system’s continuous consistency level. The strong continuous consistency ensures the same VR-SG world among different players, enabling them to make better decisions based on the individual game world’s context. Although edge computing enables a low-delay VR system for geographically dispersed players, the delay differences among players highlight the need for strong continuous consistency. Specifically, the differences in temporal and spatial dimensions among different end-players result in significant variations in their perceived end-to-end delay, further exhibiting different game worlds. We first propose a long-term task redistribution problem to enhance the continuous consistency for edge-assisted VR-SGs while controlling the consistency loss and player-perceived delay. To solve the above time-coupled problem, we design an online polynomial-time algorithm called the Online Continuous Consistency Enhancement (OCCE) algorithm. OCCE can effectively obtain the task redistribution scheme with the integrated randomized rounding and the Constraints-Firefighter Algorithm. We prove that the continuous consistency optimality of OCCE can approximate the optimal offline solution. Finally, the extensive evaluations based on real-world datasets and preparatory measurements show that, at the player scale of 30, OCCE improves continuous consistency by at least 2.38$\times$ compared to alternatives in the average case.