Analyzing viewport prediction under different VR interactions
PubDate: December 2019
Teams: AT&T Labs Research,University of Minnesota
Writers: Tan Xu, Bo Han,Feng Qian
PDF: Analyzing viewport prediction under different VR interactions
Abstract
In this paper, we study the problem of predicting a user’s viewport movement in a networked VR system (i.e., predicting which direction the viewer will look at shortly). This critical knowledge will guide the VR system through making judicious content fetching decisions, leading to efficient network bandwidth utilization (e.g., up to 35% on LTE networks as demonstrated by our previous work) and improved Quality of Experience (QoE). For this study, we collect viewport trajectory traces from 275 users who have watched popular 360° panoramic videos for a total duration of 156 hours. Leveraging our unique datasets, we compare viewport movement patterns of different interaction modes: wearing a head-mounted device, tilting a smartphone, and dragging the mouse on a PC. We then apply diverse machine learning algorithms - from simple regression to sophisticated deep learning that leverages crowd-sourced data - to analyze the performance of viewport prediction. We find that the deep learning approach is robust for all interaction modes and yields supreme performance, especially when the viewport is more challenging to predict, e.g., for a longer prediction window, or with a more dynamic movement. Overall, our analysis provides key insights on how to intelligently perform viewport prediction in networked VR systems.