Viewport-Aware Dynamic 360{\deg} Video Segment Categorization

TL;DR

This paper introduces a new viewport clustering and dynamic segment categorization method for 360-degree videos, improving the understanding of user viewport patterns to enhance streaming quality and user experience.

Contribution

It presents a novel viewport clustering algorithm and a dynamic video segment categorization approach, outperforming existing methods in capturing viewport similarities across videos.

Findings

Improved viewport clustering accuracy

Enhanced dynamic segment categorization

Better similarity within viewport clusters

Abstract

Unlike conventional videos, 360{\deg} videos give freedom to users to turn their heads, watch and interact with the content owing to its immersive spherical environment. Although these movements are arbitrary, similarities can be observed between viewport patterns of different users and different videos. Identifying such patterns can assist both content and network providers to enhance the 360{\deg} video streaming process, eventually increasing the end-user Quality of Experience (QoE). But a study on how viewport patterns display similarities across different video content, and their potential applications has not yet been done. In this paper, we present a comprehensive analysis of a dataset of 88 360{\deg} videos and propose a novel video categorization algorithm that is based on similarities of viewports. First, we propose a novel viewport clustering algorithm that outperforms the existing algorithms in terms of clustering viewports with similar positioning and speed. Next, we develop a novel and unique dynamic video segment categorization algorithm that shows notable improvement in similarity for viewport distributions within the clusters when compared to that of existing static video categorizations.