Hi! I'm Jiasi Chen, an assistant professor in the Department of Computer Science and Engineering at the University of California, Riverside. I received my Ph.D. from Princeton University (advisor Mung Chiang) and my B.S. from Columbia University. My hometown is the lovely city of Halifax, Canada. See my CV for more details (possibly outdated).

My research area is wireless networking, Internet video streaming, and augmented and virtual reality. I recently started teaching a new class, CS135: Virtual Reality. I'm also interested in network economics, sensor networks, and the Internet-of-Things. My work typically involves some combination of mathematical optimization, system design, and implementation on real testbeds.

Office: Winston Chung Hall 308
Office hours: Friday 3-5pm, or by appointment (Spring 2019)
Email: jiasi [at] cs [dot] ucr [dot] edu


360° VR video streaming: Online streaming of virtual reality 360° videos is rapidly growing, as more major content providers adopt the format to enrich user experience. In this project, we measure the characteristics of several thousand 360-degree YouTube videos. 360° videos pose a challenge for the network to stream because of their substantially higher bit rates (up to 25 Mbps in our study), but there are significant opportunities for reducing the delivered bitrate based on the user’s field of view. We also find that 360° videos have less variable bitrates and less motion than regular videos, possibly because 360° videos do not encode motion from camera pans. We believe that the traditional bandwidth requirements for non-VR video streams are now translated to responsiveness requirements for end-to-end 360° streaming architectures. [paper] [slides]

Mobile Deep Learning for AR: Deep learning shows great promise in providing more intelligence to augmented reality (AR) devices, but few AR apps use deep learning due to lack of infrastructure support. Deep learning algorithms are computationally intensive, and front-end devices such smartphones cannot deliver sufficient compute power for real-time processing. We propose a framework that ties together front-end devices with more powerful backend “helpers” (e.g., edge servers) that allow deep learning to be distributed across edge devices. We empirically measure the complex interactions between model compression, video quality, battery constraints, network data usage, and network conditions, and use this data to drive an optimization framework that satisfies the requirements of AR apps and maximizes user quality-of-experience. [paper][slides]

AVIS: Scheduling for adaptive videos over cellular networks: As the growth of mobile video traffic outpaces that of cellular network speed, industry is adopting HTTP-based adaptive video streaming technology which enables dynamic adaptation of video bit-rates to match changing network conditions. However, recent measurement studies have observed problems in fairness, stability, and efficiency of resource utilization when multiple adaptive video flows compete for bandwidth on a shared wired link. Through experiments and simulations, we confirm that such undesirable behavior manifests itself in cellular networks as well. To overcome these problems, we design an in-network resource management framework, AVIS, that schedules HTTP-based adaptive video flows on cellular networks. AVIS effectively manages the resources of a cellular base station across adaptive video flows. We implement a prototype system of AVIS and evaluate it on both a WiMAX network testbed and a LTE system simulator to show its efficacy and scalability. [paper][slides]


Names of my students (or co-advised students) are underlined.


Xukan Ran, Carter Slocum, Maria Gorlatova, Jiasi Chen, "ShareAR: Communication-efficient Multi-User Mobile Augmented Reality", ACM HotNets, 2019 (to appear).

Kittipat Apicharttrisorn, Xukan Ran, Jiasi Chen, Srikanth V. Krishnamurthy, Amit K. Roy-Chowdhury, "Frugal Following: Power Thrifty Object Detection and Tracking for Mobile Augmented Reality", ACM SenSys, 2019 (to appear). (19% acceptance rate)

Jiasi Chen, Xukan Ran, "Deep Learning with Edge Computing: A Review", Proceedings of the IEEE, 2019. (impact factor: 10.694)

Samet Oymak, Mehrdad Madavi, Jiasi Chen, "Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression", IEEE ISIT, 2019. [arxiv]

Jiasi Chen, Bharath Balasubramanian, Zhe Huang, "Liv(e)-ing on the Edge: User-Uploaded Live Streams Driven by “First-Mile” Edge Decisions", IEEE EDGE, 2019. (22% acceptance rate)


Xukan Ran, Haoliang Chen, Xiaodan Zhu, Zhenming Liu, Jiasi Chen, "DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics", IEEE INFOCOM, 2018. (19% acceptance rate) [pdf][slides]

Carlee Joe-Wong, Liang Zheng, Jiasi Chen, "Oligopoly Pricing in Wireless Networks", book chapter: Encyclopedia of Wireless Networks, eds. Xuemin Shen, Xiaodong Lin, Kuan Zhang. Springer (in press). [Table of Contents]


Shahryar Afzal, Jiasi Chen, K.K. Ramakrishnan, "Characterization of 360-degree videos", ACM SIGCOMM Workshop on Virtual and Augmented Reality Network, 2017. [pdf] [slides]

Xukan Ran, Haoliang Chen, Zhenming Liu, Jiasi Chen, "Delivering deep learning to mobile devices via offloading", ACM SIGCOMM Workshop on Virtual and Augmented Reality Network, 2017. [pdf][slides]

Suzan Bayhan, Liang Zheng, Jiasi Chen, Mario Di Francesco, Jussi Kangasharju, Mung Chiang, "Improving Cellular Capacity with White Space Offloading", WiOpt, 2017. [pdf]

Liang Zheng, Jiasi Chen, Carlee Joe-Wong, Chee Wei Tan, Mung Chiang, "An Economic Analysis of Wireless Network Infrastructure Sharing", WiOpt, 2017. [pdf]

Kittipat Apicharttrisorn, Ahmed Osama Fathy Atya, Jiasi Chen, Karthikeyan Sundaresan, and Srikanth V. Krishnamurthy, "Enhancing WiFi Throughput With PLC Extenders: A Measurement Study", Passive and Active Measurement Conference (PAM), 2017. (23% acceptance rate) [pdf]

Liang Zheng, Carlee Joe-Wong, Jiasi Chen, Christopher G. Brinton, Chee Wei Tan, Mung Chiang, "Economic Viability of a Virtual ISP", IEEE INFOCOM, 2017. (21% acceptance rate) [pdf]

Michael Wang, Jiasi Chen, Ehsan Aryafar, and Mung Chiang, "A Survey of Client-Controlled HetNets for 5G" (invited), IEEE Access, 2017. [pdf]


Tao Lin, Hongjia Li, Haiyong Xie, Jiasi Chen, Huajun Cui, Guoqiang Zhang, Wei An, Yang Li, "Performance and Implications of RAN Caching in LTE Mobile Networks: a Real Traffic Analysis", IEEE SECON, 2016. [pdf]

2015 and earlier

Jiasi Chen*, Mung Chiang, Jeffrey Erman, Guangzhi Li, KK Ramakrishnan, Rakesh Sinha, "Fair and Optimal Resource Allocation for LTE Multicast (eMBMS): Group Partitioning and Dynamics," IEEE INFOCOM, 2015. (19% acceptance rate) *The authors are in alphabetical order except for the 1st author. [pdf][slides] [video]

Xiaoli Wang, Jiasi Chen, Aveek Dutta, Mung Chiang, "Adaptive Video Streaming over Whitespace: SVC for 3-Tiered Spectrum Sharing," IEEE INFOCOM, 2015. (19% acceptance rate) [pdf]

Jiasi Chen, Amitabh Ghosh, Mung Chiang, "Mechanisms for Quota-Aware Video Adaptation," book chapter: Smart Data Pricing, ed. Soumya Sen, Carlee Joe-Wong, Sangtae Ha, Mung Chiang, John Wiley, 2014. [Amazon]

Jiasi Chen, Rajesh Mahindra, M. Amir Khojastepour, Sampath Rangarajan, Mung Chiang, "Scheduling Framework for Adaptive Video Delivery over Cellular Networks," ACM MobiCom, 2013. (14% acceptance rate) [pdf]

Jiasi Chen, Soumya Sen, David Dorsey, Mung Chiang, "A Framework for Energy-efficient Adaptive Jamming of Adversarial Communications," CISS, 2013. [pdf]

Jiasi Chen, Amitabh Ghosh, Josphat Magutt, Mung Chiang, "QAVA: Quota-Aware Video Adaptation," ACM CoNEXT, 2012. (18% acceptance rate) [pdf][slides][video]

Maria Gorlatova, Zainab Noorbhaiwala, Abraham Skolnik, John Sarik, Michael Zapas, Martin Szczodrak, Jiasi Chen, Luca Carloni, Peter Kinget, Ioannis Kymissis, Dan Rubenstein, Gil Zussman, "Prototyping Energy Harvesting Active Networked Tags: Phase II MICA Mote-based Devices (demo)," ACM MobiCom, 2010. [pdf]

Maria Gorlatova, Tarun Sharma, Deep Shrestha, Enlin Xu, Jiasi Chen, Abraham Skolnik, Dongzhen Piao, Peter Kinget, Ioannis Kymissis, Dan Rubenstein, Gil Zussman, "Prototyping Energy Harvesting Active Networked Tags (EnHANTs) with MICA2 Motes (demo)," IEEE SECON, 2010 June. [pdf]

M. Ete Chan, Jiasi Chen, Victor Chiang, X. Sherry Liu, Andrew Baik, X. Lucas Lu, Bo Huo, X. Edward Guo, "A Novel 3D Coculture Trabecular Bone Explant Model for the Study of Bone Adaptation and Mechanotransduction," World Congress on Bioengineering, July.

M. Ete Chan, Jiasi Chen, Victor Chiang, X. Sherry Liu, Andrew Baik, X. Lucas Lu, Bo Huo, X. Edward Guo, "Roles of Mechanical Stimuli and Gap Junctional Communication in Long-Term Coculture of 3D Trabecular Bone Explants," Transactions of the Orthopaedic Research Society, vol. 34, paper #53, 2009.





A few of my other interests (outside of research!) from both past and present:

Last updated March 19, 2019