A Robust Algorithm for Tile-based 360-degree Video Streaming with Uncertain FoV Estimation

TL;DR

This paper introduces a robust, stochastic optimization-based algorithm for 360-degree video streaming that effectively manages uncertain head movement and bandwidth, significantly improving user experience over baseline methods.

Contribution

The paper presents a novel robust streaming algorithm that accounts for uncertain head movement and bandwidth, providing near-optimal QoE guarantees with efficient runtime solutions.

Findings

Our algorithms outperform baselines by at least 30% in QoE.

The proposed method achieves 50% higher streaming rates under high prediction errors.

Real user traces confirm substantial QoE improvements with our approach.

Abstract

We propose a robust scheme for streaming 360-degree immersive videos to maximize the quality of experience (QoE). Our streaming approach introduces a holistic analytical framework built upon the formal method of stochastic optimization. We propose a robust algorithm which provides a streaming rate such that the video quality degrades below that rate with very low probability even in presence of uncertain head movement, and bandwidth. It assumes the knowledge of the viewing probability of different portions (tiles) of a panoramic scene. Such probabilities can be easily derived from crowdsourced measurements performed by 360 video content providers. We then propose efficient methods to solve the problem at runtime while achieving a bounded optimality gap (in terms of the QoE). We implemented our proposed approaches using emulation. Using real users' head movement traces and real cellular bandwidth traces, we show that our algorithms significantly outperform the baseline algorithms by at least in $30\%$ in the QoE metric. Our algorithm gives a streaming rate which is $50\%$ higher compared to the baseline algorithms when the prediction error is high.