ECE Seminar* Speaker: Prof. Bin Li, Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island
When: Friday,
October 23, 2020
2:00 PM
-
4:00 PM
Where: > See description for location
Cost: Free
Description: Topic: Adaptive Rate Selection for Panoramic Video Streaming Over Wireless Networks
ZOOMTELECONFERENCE:
https://umassd.zoom.com.cn/j/99000911674?pwd=N3NaZjEyZTd5MkRXSEFNNkFONUl4Zz09
Meeting ID: 990 0091 1674
Passcode: ECE599
Abstract:
Panoramic video streaming has received great attention recently due to its immersive experience. Different from traditional video streaming, it typically consumes 4-6x larger bandwidth with the same resolution. Fortunately, users can only see a portion (roughly 20%) of panoramic scenes at each time and thus it is sufficient to deliver such a portion, namely Field of View (FoV), if we can accurately predict the user's motion. In practice, we usually deliver a portion larger than FoV to tolerate inaccurate prediction. Intuitively, the larger the delivered portion, the higher the prediction accuracy. This however leads to a lower transmission success probability. The goal is to select an appropriate delivered portion to maximize system throughput, which can be formulated as a multi-armed bandit problem, where each arm represents the delivered portion. Different from traditional bandit problems with single feedback information, we have two-level feedback information (i.e., both prediction and transmission outcomes) after each decision on the selected portion. As such, we propose an efficient algorithm based on two-level feedback information, and demonstrate its superior performance than its traditional counterpart using synthetic and real datasets. This is the joint work with Jiangong Chen, Harsh Gupta and R. Srikant.
Biography:
Bin Li is the assistant professor in the Department of Electrical, Computer and Biomedical engineering at the University of Rhode Island. Prior to joining the URI faculty in August 2016, he was a postdoctoral researcher in the Coordinated Science Lab at the University of Illinois at Urbana-Champaign. He received his Ph.D. degree in Electrical and Computer Engineering from The Ohio State University in May 2014. His research focuses on the intersection of networking and machine learning, and their applications in networking for virtual/augmented reality, mobile edge computing, mobile crowd-learning, and Internet-of-Things. He received both National Science Foundation (NSF) CAREER Award and the Google Faculty Research Award in 2020.
The Seminars is open to the public free of charge.
*For further information, please contact Dr. Honggang Wang via email at hwang1@umassd.edu.
ZOOMTELECONFERENCE:
https://umassd.zoom.com.cn/j/99000911674?pwd=N3NaZjEyZTd5MkRXSEFNNkFONUl4Zz09
Meeting ID: 990 0091 1674
Passcode: ECE599
Abstract:
Panoramic video streaming has received great attention recently due to its immersive experience. Different from traditional video streaming, it typically consumes 4-6x larger bandwidth with the same resolution. Fortunately, users can only see a portion (roughly 20%) of panoramic scenes at each time and thus it is sufficient to deliver such a portion, namely Field of View (FoV), if we can accurately predict the user's motion. In practice, we usually deliver a portion larger than FoV to tolerate inaccurate prediction. Intuitively, the larger the delivered portion, the higher the prediction accuracy. This however leads to a lower transmission success probability. The goal is to select an appropriate delivered portion to maximize system throughput, which can be formulated as a multi-armed bandit problem, where each arm represents the delivered portion. Different from traditional bandit problems with single feedback information, we have two-level feedback information (i.e., both prediction and transmission outcomes) after each decision on the selected portion. As such, we propose an efficient algorithm based on two-level feedback information, and demonstrate its superior performance than its traditional counterpart using synthetic and real datasets. This is the joint work with Jiangong Chen, Harsh Gupta and R. Srikant.
Biography:
Bin Li is the assistant professor in the Department of Electrical, Computer and Biomedical engineering at the University of Rhode Island. Prior to joining the URI faculty in August 2016, he was a postdoctoral researcher in the Coordinated Science Lab at the University of Illinois at Urbana-Champaign. He received his Ph.D. degree in Electrical and Computer Engineering from The Ohio State University in May 2014. His research focuses on the intersection of networking and machine learning, and their applications in networking for virtual/augmented reality, mobile edge computing, mobile crowd-learning, and Internet-of-Things. He received both National Science Foundation (NSF) CAREER Award and the Google Faculty Research Award in 2020.
The Seminars is open to the public free of charge.
*For further information, please contact Dr. Honggang Wang via email at hwang1@umassd.edu.
Contact:
ECE: Electrical & Computer Engineering Department 508.999.9164 http://www.umassd.edu/engineering/ece/
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering