OR2M: a novel optimized resource rendering methodology for wireless networks based on virtual reality (VR) applications

Abstract

Virtual Reality (VR) applications depending on wireless networks demand low-latency representations for efficient modeling. However, the primary concern is the seamless accessibility of the resources for a sustainable VR environment. The scope of such applications is valid for its ease of modeling and swift continuity for resource utilization. The research paper proposes an Optimized Resource Rendering Method (OR2M) that accounts for the VR requirements based on latency and data rate at the initialization state. The initialization state demands maximum data at high-speed and low-latency features for generating wireless VR. The representation state demands free flow availability of wireless and cloud resources that sustain the initialization state demands. Therefore, the analysis is performed using classification tree learning to identify the VR demands in the backboned wireless networks. The consecutive learning performs classification from the unsatisfied rendering demand from the previous interval for optimizing the representation. Experimental results state that the proposed method reduces failures by 10.61% and latency by 7.28% under varying service providers.