IEEE VTS NANJING CHAPTER DISTINGUISHED LECTURE BY PROF. Celimgue WU
Abstract: As Internet-of-Vehicles (IoV) systems continue to evolve, video transmission plays a crucial role in applications such as autonomous driving. Unlike static images, video data provides richer and more continuous information, making it essential for real-time, dynamic decision-making in IoV systems. However, delivering high-quality video over wireless networks in real-time presents significant challenges, including bandwidth constraints, high latency, and degraded system performance. This talk introduces a novel semantic communication system for video streaming that leverages generative AI to significantly reduce data transmission while ensuring receiver requirements are met. Experimental results from a prototype implementation demonstrate that this approach outperforms existing methods, delivering substantial improvements in efficiency and performance.
Date and Time
Location
Hosts
Registration
- Date: 21 Apr 2025
- Time: 07:00 AM UTC to 08:30 AM UTC
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- School of Electronic Science and Engineering
- Nanjing University
- Nanjing, Jiangsu
- China
- Building: Pan Zhonglai Building
- Room Number: Room 224, Meeting Room No. 2
Speakers
Celimgue Wu of The University of Electro-Communications
Semantic Communications with Generative AI for Video Streaming Toward Efficient Remote Driving
Abstract: As Internet-of-Vehicles (IoV) systems continue to evolve, video transmission plays a crucial role in applications such as autonomous driving. Unlike static images, video data provides richer and more continuous information, making it essential for real-time, dynamic decision-making in IoV systems. However, delivering high-quality video over wireless networks in real-time presents significant challenges, including bandwidth constraints, high latency, and degraded system performance. This talk introduces a novel semantic communication system for video streaming that leverages generative AI to significantly reduce data transmission while ensuring receiver requirements are met. Experimental results from a prototype implementation demonstrate that this approach outperforms existing methods, delivering substantial improvements in efficiency and performance.
Biography:
Celimuge Wu received his PhD degree from The University of Electro-Communications, Japan. He is currently a professor and the director of Meta-Networking Research Center, The University of Electro-Communications. His research interests include Vehicular Networks, Edge Computing, IoT, and AI for Wireless Networking and Computing. He serves as an associate editor of IEEE Transactions on Cognitive Communications and Networking, IEEE Transactions on Network Science and Engineering, and IEEE Transactions on Green Communications and Networking. He is Vice Chair (Asia Pacific) of IEEE Technical Committee on Big Data (TCBD). He is a recipient of 2021 IEEE Communications Society Outstanding Paper Award, 2021 IEEE Internet of Things Journal Best Paper Award, IEEE Computer Society 2020 Best Paper Award and IEEE Computer Society 2019 Best Paper Award Runner-Up. He is an IEEE Vehicular Technology Society Distinguished Lecturer.
Email:
Address:The University of Electro-Communications, , Japan