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Adaptive 360 VR Video Streaming: Divide and Conquer
Open accessAbstract
While traditional multimedia applications such as games and videos are still popular, there has been a significant interest in the recent years towards new 3D media such as 3D immersion and Virtual Reality (VR) applications, especially 360 VR videos. 360 VR video is an immersive spherical video where the user can look around during playback. Unfortunately, 360 VR videos are extremely bandwidth intensive, and therefore are difficult to stream at acceptable quality levels. In this paper, we propose an adaptive bandwidth-efficient 360 VR video streaming system using a divide and conquer approach. We propose a dynamic view-aware adaptation technique to tackle the huge bandwidth demands of 360 VR video streaming. We spatially divide the videos into multiple tiles while encoding and packaging, use MPEG-DASH SRD to describe the spatial relationship of tiles in the 360-degree space, and prioritize the tiles in the Field of View (FoV). In order to describe such tiled representations, we extend MPEG-DASH SRD to the 3D space of 360 VR videos. We spatially partition the underlying 3D mesh, and construct an efficient 3D geometry mesh called hexaface sphere to optimally represent a tiled 360 VR video in the 3D space. Our initial evaluation results report up to 72% bandwidth savings on 360 VR video streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.
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Cited by
Viewport-adaptive navigable 360-degree video delivery
Corbillon, X., Simon, G., Devlic, A., & Chakareski, J. (2017) · 2017 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2017.7996611 · Cited by 341
State-of-the-Art in 360° Video/Image Processing: Perception, Assessment and Compression
Xu, M., Li, C., Zhang, S., & Callet, P. L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2966864 · Cited by 281
LiveDeep: Online Viewport Prediction for Live Virtual Reality Streaming Using Lifelong Deep Learning
Feng, X., Liu, Y., & Wei, S. (2020) · 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr46266.2020.00104 · Cited by 45
Lee, N., Choi, W., & Lee, S. (2021) · BMC Geriatrics · doi:10.1186/s12877-021-02263-1 · Cited by 33
Metadata
- Authors
- Hosseini, M., & Swaminathan, V.
- Venue
- 2016 IEEE International Symposium on Multimedia (ISM)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 107-110
- OpenAlex
- W2524582890
- Cited by
- 303
- References
- 0
- FWCI
- 13.51
- Type
- conference paper
- Language
- en
- Open access
- green
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