Hi! I'm a fifth-year PhD candidate in Electrical Engineering at Princeton University, advised by Prof. Mung Chiang. I received my B.S. in Electrical Engineering from Columbia University in 2010 and a Master's degree in Electrical Engineering from Princeton University in 2012. I grew up in the lovely city of Halifax, Canada. Please see my CV if you would like more details about my background.

I'm interested in multimedia streaming over Internet networks, with particular emphasis on wireless networks. By optimizing on the client, network, or server, we aim to improve quality of service for the end user. My work involves algorithm design, simulations, and real implementation of solutions to improve the performance of Internet video streaming.

Office: F310, EQuad
Mailing Address: Electrical Engineering, EQuad, Princeton University, Princeton, NJ 08544
Email: jiasic [at] princeton [dot] edu


Pricing and resource allocation for LTE multicast: With ever-increasing data traffic from mobile devices, but limited wireless resources, cellular service providers seek new ways to efficiently deliver content to users. At the same time they seek to lower the end user’s cost for consuming data, which is increasingly tied to usage-based charging models. In cases where there is spatial and temporal locality of content requests, multicasting the content using LTE’s evolved multimedia broadcast multicast service (eMBMS) is a promising approach. Although 3GPP has standardized the eMBMS architecture and signaling, service providers still need to understand how the overall revenue model impacts wireless radio channel resource allocation across multicast and unicast users. In this work, we examine the interaction between (a) ISP revenue, (b) unicast user rate, and (c) users’s channel conditions. We analyze the impact of any potential content provider subsidy of the end-user’s data consumption, the number of multicast users, and the users’ cellular link quality.

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]

QAVA: Quota-aware video adaptation: Two emerging trends of Internet applications, video traffic becoming dominant and usage-based pricing plans becoming prevalent, are at odds with each other. On one hand, videos, especially on high-resolution devices (e.g., iPhone 5, iPad, Android tablets), consume much more data than other types of traffic; for instance, 15 min of low bitrate YouTube videos per day uses 1 GB a month. On the other hand, gone are the days of unlimited data plans; instead, wireless ISPs such as AT&T and Verizon are imposing data caps on consumers. Given this conflict, a natural question to ask is: Can the consumer stay within her monthly data quota without suffering a noticeable drop in video quality? My research in this area focuses on designing algorithms to maximize the user's quality of experience and stay under the data quota, by leveraging the user's past data consumption profile and video preferences.[paper][slides][presentation]


Chen J, Ghosh A, Chiang M, "Mechanisms for Quota-Aware Video Adaptation," book chapter: Smart Data Pricing, ed. Sen S, Joe-Wong C, Ha S, Chiang M, John Wiley, 2014. [Amazon]

Chen J, Mahindra R, Khojastepour A, Rangarajan S, Chiang M, "Scheduling Framework for Adaptive Video Delivery over Cellular Networks," ACM MobiCom, 2013. (14% acceptance rate) [pdf]

Chen J, Sen S, Dorsey D, Chiang M, "A Framework for Energy-efficient Adaptive Jamming of Adversarial Communications," CISS, 2013. [pdf]

Chen J, Ghosh A, Magutt J, Chiang M, "QAVA: Quota-Aware Video Adaptation," ACM CoNEXT, 2012. (18% acceptance rate) [pdf]

Gorlatova M, Noorbhaiwala Z, Skolnik A, Sarik J, Szczodrak M, Chen J, Zapas M, Carloni L, Kinget P, Kymissis I, Rubenstein S, Zussman G, "Prototyping Energy Harvesting Active Networked Tags: Phase II MICA Mote-based Devices (demo)," ACM MobiCom, 2010. [pdf]

Gorlatova M, Sharma T, Shrestha D, Chen J, Skolnik A, Piao D, Kinget P, Kymissis J, Rubenstein D, Zussman G, "Prototyping Energy Harvesting Active Networked Tags (EnHANTs) with MICA2 Motes (demo)," IEEE SECON, 2010 June. [pdf]

Chan ME, Chen J, Chiang V, Liu XS, Baik AD, Lu XL, Huo B, Guo XE, “A Novel 3D Coculture Trabecular Bone Explant Model for the Study of Bone Adaptation and Mechanotransduction," World Congress on Bioengineering, July. [pdf]

Chan ME, Chen J, Chiang V, Liu XS, Baik AD, Lu XL, Huo B, Guo XE, "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. [pdf]


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

Last updated Oct. 6, 2014