Social-viewport adaptive caching scheme with clustering for virtual reality streaming in an edge computing platform

Abstract

This paper proposes a novel social-viewport adaptive caching scheme (SACS) for virtual reality (VR) streaming in an edge-computing platform. In VR contents with 360 degree views where only a part of the entire view (i.e., the viewport) is shown and the remaining parts are decoded but not shown, we collect and record multiple clients’ viewports of the same VR contents in local proximity on the edge-computing platform. We extract a social-viewport map, which represents where most of the local clients are directing their attention. By utilizing the social-viewport map, under our proposed scheme, k-means and mean-shift clustering algorithms are adopted to partition 360 degree views into multiple clusters with the nearest mean of hit-ratios from multiple clients. Accordingly, in order to save cache storage while maintaining a high-quality VR streaming service, we adaptively assign different encoding rates with various levels to multiple viewports. We implement the proposed scheme using a commercial EdgeX foundry edge-computing platform. A measurement-based experiment reveals that the proposed scheme achieves a maximum storage reduction of almost 74%, with a 92% hit-ratio to the highest encoded viewports.