Jun Wu is a professor in College of Electronic and Information Engineering, Tongji University. He received his B.S. degree and M.S in Information Engineering from XIDIAN University in 1993 and 1996, respectively. He received his Ph.D. degrees in Information Engineering from Beijing University of Posts and Telecomm. in 1999. Wu joined Tongji University as a Professor in Dec. 2010. He has been a principal scientist in Huawei from 2009 to 2010, and also a principal scientist in Broadcom Inc. from 2006 to 2009. His research interests include information theory, wireless communication, and digital signal processing. He has authored or co-authored over 100 papers, two chapters of a book, and filed more than 50 patents.
Wu is currently an IEEE senior member, ACM member, senior member of Chinese Institute of Electronics (CIE). He is serving as an Associate Editor of IEEE Transactions on Multimedia (TMM), Associate Editor of IEEE Wireless Communications Letters (WCL). He served as TPC Co-Chair of IEEE International Conference on Multimedia and Expo (ICME) 2019, IEEE GlobeCom 2016 Symposium Chair of Communications Software, Services and Multimedia Apps, Chinacom 2015 TPC Co-chair, IEEE ICCC 2014 Wireless Networking and Multimedia Symposium Co-chair.
Title：The Visual Communication over Wireless Networks
The tradition radio access network is built on the specific hardware platform, now is evolving towards cloud computing platform. With the new mobile edge computing (MEC) architecture, the communication and computation is converging, which brings many new opportunities and challenges for visual communication. The introduction of edge cache can reduce unnecessary traffic load and improve latency in the wireless access networks. How to optimize the caching strategy which aims to maximize the successful transmission probability (STP) of the video contents at edge base stations (BSs) is an interesting problems. We try to develop a gradient based iterative algorithm to solve the general random caching strategy optimization problem. Furthermore, the visual communication produces big data traffics. It puts forward a huge challenge to the wireless networks, while its abundant information provides potential to improve the spectrum efficiency significantly. We investigate a novel data assisted communication of mobile image (DAC-Mobi) scheme, which utilizes a large amount of correlated (similar) images stored in the cloud to improve the spectrum efficiency and visual quality.
Liang Zhou received his Ph.D. degree major at Electronic Engineering both from Ecole Normale Superieure (E.N.S.), Cachan, France and Shanghai Jiao Tong University, Shanghai, China in March 2009. Now, he is a professor in Nanjing University of Posts and Telecommunications, China.
His research interests are in the area of multimedia communications and networks, in particular, resource allocation and scheduling, cognitive and cooperative communications, cross-layer design, multimedia security, multimedia signal processing. He currently serves as an editor for IEEE Wireless Communications (2018-), IEEE Network (2018-), IEEE Transactions on Circuits and Systems for Video Technology (2013-), IEEE Transactions on Multimedia (2014-), and guest editor for IEEE Systems Journal (2011), EURASIP Journal of Wireless Communications and Networking (2011), ACM/Springer Multimedia Systems Journal (2010), and International Journal of Communications System (2010). He also serves as Co-Chair and Technical Program Committee (TPC) member for a number of international conferences and workshops (e.g., IEEE Globecom’10-12, IEEE ICC’10-12 etc.). He is a senior member of IEEE, IEEE MMTC, and IEEE MMSP.
Title: Personalized Video Streaming Strategy
Abstract: Personalized service has become the trend of network-based multimedia applications. Generally, there are two primary and essential technical challenges: i) different users usually require diverse user experiences, and ii) the network environments may vary with the time and place as well. To resolve this dilemma, this work proposes a personalized multimedia streaming strategy by intelligently categorizing the user via the data mining and adaptively predicting the transmission fashion in real time via the reinforcement learning. Specifically, on one hand, a class-level joint user classification and data cleaning scheme is proposed by frequently updating the training processes. On the other hand, a neural network model is constructed by making use of the reinforcement learning. As such, the video rate in the future can react quickly through the neural network model even if in a dynamic environment. Moreover, the objective representation of user experience is modelled from the user class instead of user himself, and it is used as the incentive information to train and improve the above neural network model. Extensive results validate the efficiency of the proposed scheme.