NAVA: A Network-Adaptive View-Aware Volumetric Video Streaming Framework
Volumetric video is the emerging format for representing real-world dynamic objects such as humans in Extended Reality (XR) applications. However, real-time streaming of volumetric video to user devices is challenging due to the extremely high data rate and low latency requirements. This paper intro...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870276/ |
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| author | Nguyen Long Quang Truong Thu Huong Duc Nguyen |
| author_facet | Nguyen Long Quang Truong Thu Huong Duc Nguyen |
| author_sort | Nguyen Long Quang |
| collection | DOAJ |
| description | Volumetric video is the emerging format for representing real-world dynamic objects such as humans in Extended Reality (XR) applications. However, real-time streaming of volumetric video to user devices is challenging due to the extremely high data rate and low latency requirements. This paper introduces NAVA, a novel network-adaptive view-aware volumetric video streaming framework for XR scenes consisting of multiple volumetric sequences. The proposed framework dynamically adapts the quality of individual volumetric sequences based on network conditions and the user’s viewpoint to optimize streaming performance under network constraints. In our framework, multiple versions with different quality of individual volumetric video are prepared and stored on the server in advance. The rate allocation problem is formulated as a optimization problem by taking into account the visible area of individual sequences as well as the network constraint. We then present two solutions to decide the quality of each volumetric video in real-time. Extensive evaluation shows that the proposed framework can increase the viewport quality by <inline-formula> <tex-math notation="LaTeX">$0.5\sim 1.1$ </tex-math></inline-formula>dB compared to existing methods. The outcome of this study is expected to accelerate the adoption of real-time interactive XR applications, enabling users to experience and interact with dynamic virtual environments seamlessly. |
| format | Article |
| id | doaj-art-829628bc7cf5404c9f738399fa03994d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-829628bc7cf5404c9f738399fa03994d2025-02-12T00:02:27ZengIEEEIEEE Access2169-35362025-01-0113252232523810.1109/ACCESS.2025.353880210870276NAVA: A Network-Adaptive View-Aware Volumetric Video Streaming FrameworkNguyen Long Quang0https://orcid.org/0009-0009-8395-6545Truong Thu Huong1https://orcid.org/0000-0002-6428-8539Duc Nguyen2https://orcid.org/0000-0003-1122-0650School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, VietnamSchool of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, VietnamDepartment of Information and Communication Engineering, Tohoku Institute of Technology, Sendai, JapanVolumetric video is the emerging format for representing real-world dynamic objects such as humans in Extended Reality (XR) applications. However, real-time streaming of volumetric video to user devices is challenging due to the extremely high data rate and low latency requirements. This paper introduces NAVA, a novel network-adaptive view-aware volumetric video streaming framework for XR scenes consisting of multiple volumetric sequences. The proposed framework dynamically adapts the quality of individual volumetric sequences based on network conditions and the user’s viewpoint to optimize streaming performance under network constraints. In our framework, multiple versions with different quality of individual volumetric video are prepared and stored on the server in advance. The rate allocation problem is formulated as a optimization problem by taking into account the visible area of individual sequences as well as the network constraint. We then present two solutions to decide the quality of each volumetric video in real-time. Extensive evaluation shows that the proposed framework can increase the viewport quality by <inline-formula> <tex-math notation="LaTeX">$0.5\sim 1.1$ </tex-math></inline-formula>dB compared to existing methods. The outcome of this study is expected to accelerate the adoption of real-time interactive XR applications, enabling users to experience and interact with dynamic virtual environments seamlessly.https://ieeexplore.ieee.org/document/10870276/Extended realityvolumetric videoview-aware streaminglevel of details |
| spellingShingle | Nguyen Long Quang Truong Thu Huong Duc Nguyen NAVA: A Network-Adaptive View-Aware Volumetric Video Streaming Framework IEEE Access Extended reality volumetric video view-aware streaming level of details |
| title | NAVA: A Network-Adaptive View-Aware Volumetric Video Streaming Framework |
| title_full | NAVA: A Network-Adaptive View-Aware Volumetric Video Streaming Framework |
| title_fullStr | NAVA: A Network-Adaptive View-Aware Volumetric Video Streaming Framework |
| title_full_unstemmed | NAVA: A Network-Adaptive View-Aware Volumetric Video Streaming Framework |
| title_short | NAVA: A Network-Adaptive View-Aware Volumetric Video Streaming Framework |
| title_sort | nava a network adaptive view aware volumetric video streaming framework |
| topic | Extended reality volumetric video view-aware streaming level of details |
| url | https://ieeexplore.ieee.org/document/10870276/ |
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