Viewport Adaptation-Based Immersive Video Streaming: Perceptual Modeling and Applications
This work addresses bandwidth efficiency for immersive video streaming users, representing an incremental improvement with a specific modeling approach.
The paper tackles the problem of optimizing immersive video streaming under limited bandwidth by modeling the perceptual impact of quality variations during viewport adaptation, resulting in a 9.36% BD-Rate improvement on average compared to other methods.
Immersive video offers the freedom to navigate inside virtualized environment. Instead of streaming the bulky immersive videos entirely, a viewport (also referred to as field of view, FoV) adaptive streaming is preferred. We often stream the high-quality content within current viewport, while reducing the quality of representation elsewhere to save the network bandwidth consumption. Consider that we could refine the quality when focusing on a new FoV, in this paper, we model the perceptual impact of the quality variations (through adapting the quantization stepsize and spatial resolution) with respect to the refinement duration, and yield a product of two closed-form exponential functions that well explain the joint quantization and resolution induced quality impact. Analytical model is cross-validated using another set of data, where both Pearson and Spearman's rank correlation coefficients are close to 0.98. Our work is devised to optimize the adaptive FoV streaming of the immersive video under limited network resource. Numerical results show that our proposed model significantly improves the quality of experience of users, with about 9.36\% BD-Rate (Bjontegaard Delta Rate) improvement on average as compared to other representative methods, particularly under the limited bandwidth.