ABSTRACT
Augmented reality/virtual reality (AR/VR) data require huge seamless bandwidth and low latency to reduce computational connectivity issues. Mobile edge computation (MEC) reduces the latencies in crucial decisions. Data slicing and edge computing allow for prioritizing the data download and upload. Edge computing offers storage and computational resources at the edges of networks. Edge devices simulate a framework for data prediction and slicing model to slice AR/VR data streaming. AR/VR slicing model requires uploading and downloading streams speed limit, connectivity time, bandwidth, and user pattern as its parameters to predict data slicing model in edge computing to improvise the network utilization. MEC uses machine learning for three aspects: (1) prediction for offloading; (2) scheduling; and (3) resource allocation.
