RAM360: Robust Adaptive Multi-Layer 360$^\circ$ Video Streaming With Lyapunov Optimization
Haodan Zhang, Yixuan Ban, Zongming Guo, Ken Chen, Xinggong Zhang
Abstract
Viewport-adaptive streaming approaches are emerging as the most promising way to deliver high-quality 360 videos over mobile networks. However, the viewport prediction is only reliable within a short prediction window, i.e., a short playback buffer, which conicts with maintaining a long buffer to avoid playback rebuffering. To deal with this problem, we present RAM360, a Robust Adaptive Multi-layer 360 video streaming system, to ensure high viewport quality and low stall ratio concurrently. We make three technical contributions. First, we design a QoE-driven robust multi-layer streaming framework, where each chunk is encoded by multiple independent layers with different quality levels. The client dynamically decides which chunk and layer to be downloaded by their QoE contributions. Thus, the base-layer could be prefetched to avoid the risk of stalling while the viewport quality is improved by downloading enhancement layer. Second, we establish a novel QoE model to represent the quality of whole playback session, not that of chunk. It aims to maximize the overall QoE of playback session. Third, we introduce the Lyapunov optimization theory to solve the QoE optimization problem, which is an online algorithm with near-optimality solution. We demonstrate that RAM360 can significantly outperform the existing schemes regarding viewport quality, stall ratio, and QoE through extensive experiments with public datasets.