A Rate Adaptation Algorithm for Tile-based 360-degree Video Streaming

TL;DR

This paper introduces a novel adaptive streaming algorithm for 360-degree videos that intelligently allocates bandwidth by predicting user head movement and bandwidth, significantly improving user experience.

Contribution

It presents a low-complexity, optimality-proven algorithm that adaptively adjusts video quality based on user head movement and bandwidth prediction in 360-degree streaming.

Findings

QoE improved by at least 20% over baselines

Algorithm achieves optimality under practical assumptions

Effectively balances quality and bandwidth in 360-degree videos

Abstract

In the 360-degree immersive video, a user only views a part of the entire raw video frame based on her viewing direction. However, today's 360-degree video players always fetch the entire panoramic view regardless of users' head movement, leading to significant bandwidth waste that can be potentially avoided. In this paper, we propose a novel adaptive streaming scheme for 360-degree videos. The basic idea is to fetch the invisible portion of a video at the lowest quality based on users' head movement prediction and to adaptively decide the video playback quality for the visible portion based on bandwidth prediction. Doing both in a robust manner requires overcome a series of challenges, such as jointly considering the spatial and temporal domains, tolerating prediction errors, and achieving low complexity. To overcome these challenges, we first define quality of experience (QoE) metrics for adaptive 360-degree video streaming. We then formulate an optimization problem and solve it at a low complexity. The algorithm strategically leverages both future bandwidth and the distribution of users' head positions to determine the quality level of each tile (i.e., a sub-area of a raw frame). We further provide theoretical proof showing that our algorithm achieves optimality under practical assumptions. Numerical results show that our proposed algorithms significantly boost the user QoE by at least 20\% compared to baseline algorithms.