Trend Aware Proactive Caching of Online Video

时间:2013-08-07 点击:



报告题目:Trend Aware Proactive Caching of Online Video

报告时间:8月7日 10:00


报告人:Wenjun (Kevin) Zeng


Wenjun (Kevin) Zeng is a Full Professor with the Computer Science Department of University of Missouri, Columbia, MO. He received his B.E., M.S., and Ph.D. degrees from Tsinghua University, the University of Notre Dame, and Princeton University, respectively, all in electrical engineering. His current research interest includes mobile computing, social media analysis, semantic search, distributed source/video coding, 3-D analysis and coding, multimedia networking, and content and network security.
Prior to joining Univ. of Missouri in 2003, he had worked for PacketVideo Corp., Sharp Labs of America, Bell Labs, and Panasonic Technology. From 1998 to 2002, He was an active contributor to the JPEG 2000 and MPEG4 IPMP standard, where four of his proposals were adopted. He is/was an Associate Editor (AE) of IEEE Trans. on Circuits & Systems for Video Technology,IEEE Multimedia, IEEE Trans. on Information Forensics & Security, and IEEE Trans. on Multimedia (TMM), and was on the Steering Committee of IEEE TMM from 2009-2012.He is serving as the TPC co-Chair of the 2013 IEEE Inter. Workshop on Info. Forensics &Security, and a Guest Editor of ACM TOMCCAP Special Issue on ACM MM 2012 Best Papers. He served as the Steering Committee Chair of IEEE Inter. Conf. Multimedia and Expo (ICME) in 2010 and 2011,and has served as the TPC Vice Chair of ICME 2009, the TPC Chair of the IEEE CCNC 2007, the TPC Co-Chair of the Multimedia Comm. and Home Networking Sym. of IEEE ICC 2005. He was a Guest Editor (GE) of the Proceedings of the IEEE’s Special Issue on Recent Advances in Distributed Multimedia Communications (January 2008) and the Lead GE of IEEE TMM’s Special Issue on Streaming Media (April 2004). He is a Fellow of the IEEE.


In the recent years, popularity of social media and online video sharing services has grown at an unprecedentedly fast pace. Modern Internet faces new challenges with a growing demand on video. Caching content has been an effective method for improving quality of service for online video. In this talk, I will present a mainstream media driven trend detection and caching framework that transits the knowledge of detected trends to online video sharing portals, to detect emerging popular videos, and pre-cache them at strategically deployed caching nodes. In particular, we propose to explore a combination of topic modeling and frequent pattern mining to design a cross-platform video popularity prediction scheme. We further propose a trend-aware and reputation-based video-ranking algorithm to select correct caching candidates among a large array of redundant content for proactive caching by the Internet Service Providers (ISP).