Xiaoli Wang, Shirley      

Office: C319A, EQuad
Address: Electrical Engineering, EQuad, Princeton University, Princeton, NJ, 08544
E-mail: xw4 [at] princeton [dot] edu



I'm Xiaoli (Shirley) Wang, a Ph.D. candicate in Electrical Engineering at Princeton University, advised by Dr. Mung Chiang. I received my BEng in Electronic Engineering from The Hong Kong University of Science and Technology (HKUST) in 2012 and a Master's degree in Electrical Engineering from Princeton University in 2014. I'm generally interested in wireless communication and mobile network, with emphasis on Internet of Things and network edge analytics. My work involves algorithm design, simulation and real system implementation. See my CV for details.



SkyEyes: Adaptive Video Streaming from UAVs [HotWireless 2016]

UAVs (unmanned aerial vehicles) equipped with high-end cameras have become increasingly popular among consumers. UAVs have been traditionally considered for applications such as disaster response and surveillance, while emerging applications include live-event broadcasts, precision agriculture, and augmented-reality games. Consumer UAVs today use fixed-bitrate video streaming where users configure the resolution (4K or 1080p). However, applications with realtime streaming that deploy UAVs in the wild will require adaptive video streaming to tackle uncertain wireless link capacities and meet their video quality requirements. This paper is a first step toward the design of adaptive video streaming algorithms that can provide significant gains for UAV streaming. Our system SkyEyes leverages two novel aspects to aid UAV streaming: content-based compression and video rate adaptation based on location sensors and client buffer status.

Channel Condition Prediction for Mobile Devices [WCNC 2017]

Predicting medium (100 msec to secs) and long term (seconds to 100-s of seconds) wireless link throughput is a very important and challenging problem. With such prediction available, bandwidth hungry and delay sensitive mobile applications (like streaming video) can efficiently and gracefully adapt their bandwidth demands which can result in significant improvement of end user quality of experience and much better utilization of wireless resources. One of the key components of the throughput prediction is predicting UE channel conditions.

In this work, we analyze the impact of neighboring cell load, user speed and direction of movement on the expected channel conditions. As a metric characterizing channel conditions we use an average number of useful (without retransmissions) bits per LTE physical resource block. We show how to draw a dynamic coverage map with the corresponding confidence intervals for both stationary and moving users, given the neighboring cell load. Then we outline the procedure for providing link level throughput prediction given UE pixel location and serving cell load characteristics.

IoT Session Management [IEEE IoT Journal 2016]

To efficiently support and manage massive number of IoT short and bursty sessions, current LTE system needs to reduce signal load generated by IoT session setup/synchronization, while balancing the system performance, such as UE power consumption and delays to crucial traffic. In LTE, Radio Resource Control (RRC) and Discontinuous Reception (DRX) affect power consumption, signal load and delay. To address this problem, we provide a session management methodology suitable for IoT traffic over LTE. Our analysis starts with a Markov Chain analysis of the impact of DRX parameters. This is followed by an optimal uplink scheduler design and an IoT-aware adaptive DRX algorithm at the client, both of which modulate the tradeoff among signal load, delay and power consumption. Scalability is also considered in this work by providing a high-priority clustering-based adaptive DRX algorithm at eNB.

Adaptive Video Streaming in Whitespace [INFOCOM 2015]

The recently proposed 3-Tier access model for Whitespace by the Federal Communications Commission (FCC) mandates certain classes of devices to share frequency bands in space and time. These devices are envisioned to be a heterogeneous mixture of licensed (Tier-1 and Tier-2) and unlicensed, opportunistic devices (Tier-3) . The hierarchy in accessing the channel calls for superior adaptation of Tier-3 devices with varying spectral opportunity. While policies are being ratified for efficient sharing, it also calls for redesigning many common applications to adapt to this novel paradigm.

In this work, we focus on the ever-increasing demand for video streaming and present a methodology suitable for Tier-3 devices in the shared access model. Our analysis begins with a stress test of commonly adopted video streaming methods under the new sharing model. This is followed by the design of a robust MDP-based solution that proactively adapts to fast-varying channel conditions, providing better user quality of experience when compared to existing solutions, such as MPEG-DASH. We evaluate our solution on an experimental testbed and find that our MDP-based algorithm outperforms DASH, and partial information of Tier-2 dynamics improves video quality.


  1. On Medium and Long Term Channel Conditions Prediction for Mobile Devices

    Xiaoli Wang, Edward Grinshpun, David Faucher, Sameer Sharma

    IEEE Wireless Communications and Networking Conference (WCNC 2017)

  2. SkyEyes: Adaptive Video Streaming from UAVs

    Xiaoli Wang, Aakanksha Chowdhery, Mung Chiang

    3rd ACM workshop on Hot topics in wireless (HotWireless 2016) [Invited]

  3. Internet of Things Session Management over LTE - Balancing Signal Load, Power and Delay

    Xiaoli Wang, Ming-Jye Sheng, Yuan-Yao Lou, Yuan-Yao Shih, Mung Chiang

    IEEE Internet of Things Journal 3(3): 339-353 (2016)

  4. Adaptive Video Streaming over Whitespace: SVC for 3-Tiered Spectrum Sharing

    Xiaoli Wang, Jiasi Chen, Aveek Dutta, Mung Chiang

    IEEE Conference on Computer Communications (INFOCOM 2015)

  5. Mobility Tracking using GPS, Wi-Fi and Cell ID

    Xiaoli Wang, Albert Kai-Sun Wong, Yongping Kong

    27th International Conference on Information Networking (ICOIN 2012)