Xiaoli Wang, Shirley      

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

 

About

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.

Education

Research

Networked Drone Cameras for Sports Streaming [HotWireless 2016][ICDCS 2017]

A network of drone cameras can be deployed to cover live events, such as high-action sports game played on a large field, but managing networked drone cameras in real-time is challenging. Distributed approaches yield suboptimal solutions from lack of coordination but coordination with a centralized controller incurs round-trip latencies of several hundreds of milliseconds over a wireless channel. We propose a fog-networking based system architecture to automatically coordinate a network of drones equipped with cameras to capture and broadcast the dynamically changing scenes of interest in a sports game. We design both optimal and practical algorithms to balance the tradeoff between two metrics: coverage of the most important scenes and streamed video bitrate. To compensate for network round-trip latencies, the centralized controller uses a predictive approach to predict which locations the drones should cover next. The controller maximizes video bitrate by associating each drone to an optimally matched server and dynamically re-assigns drones as relay nodes to boost the throughput in low-throughput scenarios. This dynamic assignment at centralized controller occurs at slower time-scale permitted by round-trip latencies, while the predictive approach and drones' local decision ensures that the system works in real-time. Experimental results over tens of flights on the field suggest our system can achieve really good performance, for example, 8 drones can achieve a tradeoff of $94\%$ coverage and (on average) 2K video support at 20~Mbps by optimizing between coverage and throughput. By dynamically allocating drones to cover the game or act as relays, our system also demonstrates a 2x gain over systems maximizing static coverage alone that achieves only 9~Mbps video throughput.

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 work 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.

Publication

  1. Networked Drone Cameras for Sports Streaming

    Xiaoli Wang, Aakanksha Chowdhery, Mung Chiang

    International Conference on Distributed Computing Systems (ICDCS 2017) (16.9% acceptance rate)

  2. 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)

  3. SkyEyes: Adaptive Video Streaming from UAVs

    Xiaoli Wang, Aakanksha Chowdhery, Mung Chiang

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

  4. 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)

  5. 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) (19% acceptance rate)

  6. 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)