2021 Fiscal Year Annual Research Report
AI-empowered Point Cloud Video Streaming
| Project/Area Number |
20H04174
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| Research Institution | The University of Electro-Communications |
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Principal Investigator |
劉 志 電気通信大学, 大学院情報理工学研究科, 准教授 (90750240)
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| Co-Investigator(Kenkyū-buntansha) |
太田 香 室蘭工業大学, 大学院工学研究科, 文部科学省卓越研究員(准教授) (50713971)
李 鶴 室蘭工業大学, 大学院工学研究科, 文部科学省卓越研究員(助教) (40759891)
Kien Nguyen 千葉大学, 大学院工学研究院, 助教 (80647222)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | point cloud / video streaming / MEC / AI / VR/AR/MR |
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| Outline of Annual Research Achievements |
Point cloud video is the most popular representation of hologram, which is the medium to precedent natural content in VR/AR/MR, and is expected to be the next generation video by providing users with better immersive viewing experience. Point cloud video has wide applications in Society 5.0 such as for online education and entertainment. To further enhance these applications, networked point cloud video is in critical demand. This project aims to solve the core technical questions for a high quality point cloud video streaming and promote the applications of point cloud video in Society 5.0.
Towards this goal, we have mainly considered 1) how to design transmission friendly point cloud video codec, 2) how to utilize AI to build MEC for the point cloud video streaming, and 3) how to utilize AI to schedule the point cloud video streaming, in order to provide users with high quality point cloud video services.
Part of the results have been published in IEEE journals and conferences.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
According to the schedule, we focus on the survey, the codec design, MEC and transmission scheduling in the first two years. From our achievement, we can find the progress is smooth.
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| Strategy for Future Research Activity |
Next year I will focus on the following three technical challenges:
1. AI-empowered point cloud video transmission
2. AI-inspired MEC design for point cloud video transmiission
3. prototype building and scheme adjustment
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