讲座信息
5.20 | Towards More Scalable, Low-Cost, Efficient and Intelligent Live Media Streaming Solutions
2019.05.17
演讲者 Feng Wang
头衔职位 Professor,University of Mississippi, USA
时间 2019 年 5 月 20 日 (星期一) 下午 3:30-4:30
地点 张江校区计算机楼 405
联系人 陈阳 chenyang@fudan.edu.cn

演讲简介

Live media streaming has become one of the most popular applications over the Internet. We have witnessed the successful deployment of commercial systems with CDN- or peer-to-peer based engines. While each being effective in certain aspects, having an all-round scalable, reliable, responsive and cost-effective solution remains an illusive goal. Moreover, today's multimedia streaming services have become highly globalized, with subscribers from all over the world. Such a globalization makes user behaviors and demands even more diverse and dynamic, further challenging state-of-the-art system designs. The emergences of new research frontiers such as cloud computing, edge computing and deep learning however shed new lights into this dilemma, enabling us to design a series of more scalable, low-cost, efficient and intelligent live media streaming solutions to be presented in this talk. From the global perspective, by leveraging the elastic resource provisioning from cloud, we propose CALMS (Cloud-Assisted Live Media Streaming), a generic framework that facilitates the migration and deployment of global live media streaming to the cloud. We then further expand the framework to afford crowdsourced live streaming broadcast (crowdcast), which also takes into account the dynamics from the crowd of live streaming sources. From local perspective, we examine the issues presented at the network edges in the city scale, proposing DeepCast, an intelligent edge-assisted crowdcast framework that offers versatile accommodations for personalized QoE with minimized system cost and utilizes deep reinforcement learning (DRL) to tackle the key challenges therein. At the end of this talk, our other research directions as well as future works will also be briefly discussed.

关于讲者

Feng Wang received both the Bachelor’s degree and Master’s degree in Computer Science and Technology from Tsinghua University, Beijing, China in 2002 and 2005, respectively. He received the PhD degree in Computing Science from Simon Fraser University, Burnaby, British Columbia, Canada in 2012. He is currently an Associate Professor in the Department of Computer and Information Science at the University of Mississippi, University, MS, USA. His research interests include cloud/edge computing, socialized content sharing, peer-to-peer networks, big data, wireless mesh/sensor networks, internet of things, cyber-physical systems, crowdsourcing, smart cities and deep learning. He is a senior member of IEEE. He is a recipient of IEEE ICME Quality Reviewer Award (2011) and ACM BuildSys Best Paper Award (2018). He is a Technical Committee Member of Elsevier Computer Communications. He served as Program Vice Chair in International Conference on Internet of Vehicles (IOV) 2014, and as TPC co-chair in IEEE CloudCom 2017 for Internet of Things and Mobile on Cloud track. He also serves as TPC member in various international conferences such as IEEE INFOCOM, IEEE/ACM IWQoS, ACM Multimedia, IEEE ICC, IEEE GLOBECOM and IEEE ICME.
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