LAUSR

Despite the growing popularity of Virtual Reality (VR), 360-degree videos are often regarded as challenging to stream due to their large bandwidth requirement. As a solution, the 360-degree video content is spatially divided into tiles, and the quality level for each tile is selected based on the user’s network environment and viewport information. To determine the high quality tiles, viewport prediction and viewport history methods are used to estimate the user’s viewport. However, due to the unpredictability of user head movements, generating accurate viewport estimates are difficult, which can severely degrade the Quality of Experience (QoE) for the user. In this paper, to sustain high user QoE, we detail a novel tile quality selection algorithm that employs viewport prediction, viewport history, viewport extensions, and a viewport tile count limit. In addition, we also include comparison analysis on six 360-degree videos that vary in content pace. Based from simulations, using viewport history as a heuristic for tile quality selection demonstrated a significant increase in the perceived quality while suppressing quality variation inside the viewport and across segments compared to eight reference methods; and secondly, 360-degree videos slow in content pace tended to result in lower viewport prediction accuracy, QoE performance, and weaker viewport history trends compared to 360-degree videos fast in content pace.