Viewport-Aware Dynamic 360° Video Segment Categorization
This work addresses the need for better 360° video streaming quality of experience for end-users, representing an incremental advancement in video categorization.
The paper tackled the problem of improving 360° video streaming by analyzing viewport patterns across 88 videos, proposing a novel dynamic video segment categorization algorithm that shows notable improvement in similarity for viewport distributions within clusters compared to existing static methods.
Unlike conventional videos, 360° 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° 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° 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.