• Synopsis
    • CAREER: Improving Mobile Video Delivery for Emerging Contents and Networks (NSF Award 1915122)
    Streaming videos wirelessly on mobile devices is an increasingly important application. The objective of this project is to bring innovations to mobile video delivery for new content types and over emerging networks. Specifically, the project investigates three aspects: (1) 360-degree immersive video delivery, (2) video streaming over multiple network paths (multipath), and (3) video streaming over millimeter-wave (mmWave) links. These are expected to be the key building blocks of next-generation video streaming services. First, 360-degree videos provide users with unique panoramic viewing experience; however, 360-degree video content delivery is much more challenging compared to regular videos. Second, multiple network interfaces have become a norm on off-the-shelf mobile devices but their potential is far from being fully exploited. Third, mmWave is a key technology that will be integrated into 5G wireless networks; but adapting video streaming to mmWave largely remains an uncharted territory. The proposed solutions will benefit the society by enhancing the user experience and reducing the resource consumption for next-generation immersive video services. The research will also be integrated with an education plan that seeks to prepare computer science students with the knowledge of new technological trends in networking and systems, and stimulate the general public interest in Science, Technology, Engineering, and Mathematics.
  • Project Publications
    • NSDI24 Habitus: Boosting Mobile Immersive Content Delivery through Full-body Pose Tracking and Multipath Networkingdownload
    Anlan Zhang, Chendong Wang, Yuming Hu, Ahmad Hassan, Zejun Zhang, Bo Han, Feng Qian, and Shichang Xu.
    In USENIX NSDI 2024, Santa Clara, CA.
    HotMobile24 The Case for Boosting Mobile Application QoE via Smart Band Switching in 5G/xG Networksdownload
    Ahmad Hassan, Jason Carpenter, Shuowei Jin, Ruiyang Zhu, Wei Ye, Anlan Zhang, Feng Qian, Z. Morley Mao, and Zhi-Li Zhang.
    In ACM HotMobile 2024, San Diego, CA.
    CHI23 Collaborative Online Learning with VR Video: Roles of Collaborative Tools and Shared Video Controldownload
    Qiao Jin, Yu Liu, Ruixuan Sun, Chen Chen, Puqi Zhou, Bo Han, Feng Qian, and Svetlana Yarosh.
    In ACM CHI 2023, Hamburg, Germany.
    PAM23 An In-Depth Measurement Analysis of 5G mmWave PHY Latency and its Impact on End-to-End Delaydownload
    Rostand Fezeu, Eman Ramadan, Wei Ye, Benjamin Minneci, Jack Xie, Arvind Narayanan, Ahmad Hassan, Feng Qian, Zhi-Li Zhang, Jaideep Chandrashekar, and Myungjin Lee.
    In Passive and Active Measurement Conference (PAM) 2023, Virtual Conference.
    SIGCOMM22 Vivisecting Mobility Management in 5G Cellular Networksdownload
    Ahmad Hassan, Shuowei Jin, Arvind Narayanan, Ruiyang Zhu, Anlan Zhang, Wei Ye, Jason Carpenter, Z. Morley Mao, Zhi-Li Zhang, and Feng Qian.
    In ACM SIGCOMM 2022, Amsterdam, Netherlands.
    CHI22 How Will VR Enter University Classrooms? Multi-stakeholders Investigation of VR in Higher Educationdownload
    Qiao Jin, Yu Liu, Svetlana Yarosh, Bo Han, and Feng Qian.
    In ACM CHI 2022, New Orleans, LA.
    MobiCom22 Vues: Practical Volumetric Video Streaming through Multiview Transcodingdownload
    Yu Liu, Bo Han, Feng Qian, Arvind Narayanan, and Zhi-Li Zhang.
    In ACM MobiCom 2022, Sydney, Australia.
    NSDI22 YuZu: Neural-Enhanced Volumetric Video Streaming download
    Anlan Zhang, Chendong Wang, Bo Han, and Feng Qian.
    In USENIX NSDI 2022, Renton, WA.
    MobiCom21 EMP: Edge-assisted Multi-vehicle Perceptiondownload
    Xumiao Zhang, Anlan Zhang, Jiachen Sun, Xiao Zhu, Yihua Guo, Feng Qian, and Z. Morley Mao.
    In ACM MobiCom 2021, New Orleans, LA.
    SIGCOMM21 A Variegated Look at 5G in the Wild: Performance, Power, and QoE Implicationsdownload
    Arvind Narayanan*, Xumiao Zhang*, Ruiyang Zhu, Ahmad Hassan, Shuowei Jin, Xiao Zhu, Xiaoxuan Zhang, Denis Rybkin, Zhengxuan Yang, Z. Morley Mao, Feng Qian, and Zhi-Li Zhang.
    (* Co-primary authors).
    In ACM SIGCOMM 2021, Virtual Conference.
    WWW21 DeepVista: 16K Panoramic Cinema on Your Mobile Device download
    Wenxiao Zhang, Feng Qian, Bo Han, and Pan Hui.
    In the Web Conference (WWW) 2021.
    IMC20 Lumos5G: Mapping and Predicting Commercial mmWave 5G Throughputdownload
    Arvind Narayanan, Eman Ramadan, Rishabh Mehta, Xinyue Hu, Qingxu Liu, Udhaya Kumar Dayalan, Rostand Fezeu, Saurabh Verma, Peiqi Ji, Tao Li, Feng Qian, and Zhi-Li Zhang.
    In ACM Internet Measurement Conference (IMC) 2020, Pittsburgh, PA.
    ATC20 Firefly: Untethered Multi-user VR for Commodity Mobile Devicesdownload
    Xing Liu, Christina Vlachou, Feng Qian, Chendong Wang, and Kyu-Han Kim.
    In USENIX ATC 2020, Boston, MA.
    MobiSys20 MPBond: Efficient Network-level Collaboration Among Personal Mobile Devicesdownload
    Xiao Zhu, Jiachen Sun, Xumiao Zhang, Yihua Guo, Feng Qian, and Z. Morley Mao.
    In MobiSys 2020, Toronto, Canada.
    WWW20 A First Measurement Study of Commercial mmWave 5G Performance on Smartphonesdownload
    Arvind Narayanan, Eman Ramadan, Jason Carpenter, Qingxu Liu, Yu Liu, Feng Qian, and Zhi-Li Zhang.
    In the Web Conference (WWW) 2020, Taipei, Taiwan.
    MMSys19 LIME: Understanding Commercial 360-degree Live Video Streaming Servicesdownload
    Xing Liu, Bo Han, Feng Qian, and Matteo Varvello.
    To appear in ACM MMSys 2019, Amherst, MA.
    MMSys19 Quality-aware Strategies for Optimizing ABR Video Streaming QoE and Reducing Data Usagedownload
    Yanyuan Qin, Shuai Hao, Krishna Pattipati, Feng Qian, Subhabrata Sen, Bing Wang, and Chaoqun Yue.
    To appear in ACM MMSys 2019, Amherst, MA.
    CoNEXT19 Analyzing Viewport Prediction Under Different VR Interactionsdownload
    Tan Xu, Bo Han, and Feng Qian.
    In ACM CoNEXT 2019, Orlando, FL.
    MobiCom18 (Demo) Demo: Tile-Based Viewport-Adaptive Panoramic Video Streaming on Smartphonesdownload
    Feng Qian, Bo Han, Qingyang Xiao, and Vijay Gopalakrishnan.
    In ACM MobiCom 2018, New Delhi, India.
    MobiCom18 Flare: Practical Viewport-Adaptive 360-Degree Video Streaming for Mobile Devicesdownload
    Feng Qian, Bo Han, Qingyang Xiao, and Vijay Gopalakrishnan.
    In ACM MobiCom 2018, New Delhi, India.
  • Personnel
    • PI: Feng Qian (fengqian at usc.edu)
    Current Students: Ahmad Hassan (Ph.D.), Yu Liu (Ph.D.), Anlan Zhang (Ph.D.) Past Students: Xing Liu (Ph.D.), Arvind Narayanan (Ph.D.), Runsheng Ma (undergraduate)
  • Collaborators
    • AT&T Labs Research, HP Labs, University of Connecticut, George Mason University, University of Michigan, University of Minnesota, Google
  • Data and Code
    • (1) User Head Movement Traces of 360 Videos
    The dataset consists of 5 users' head movement traces when watching 4 360-degree videos. The data was collected by us, and used in our All Things Cellular paper. [Download Dataset, 9.8MB]
    (2) 5G Measurement Data
    We release the data collected by us in the WWW20 paper. This data is important in providing a first impression of the world's very first commercial 5G rollouts, and serves as an important baseline of 5G performance. We conduct several experiments to evaluate 5G performance, including but not limited to throughput performance, latency measurements, impact of mobility and obstructions, handoff analysis among many others. Our experiments also illustrate the pros and cons of the different 5G technologies. [Goto download page]
    (3) Lumos5G Data
    We release the data collected by us in the IMC20 paper. The emerging 5G services offer numerous new opportunities for networked applications. In this study, we seek to answer two key questions: (i) is the throughput of mmWave 5G predictable, and (ii) can we build "good" machine learning models for 5G throughput prediction? To this end, we conduct a measurement study of commercial mmWave 5G services in a major U.S. city, focusing on the throughput as perceived by applications running on user equipment (UE). Through extensive experiments and statistical analysis, we identify key UE-side factors that affect 5G performance and quantify to what extent the 5G throughput can be predicted. We then propose Lumos5G -- a composable machine learning (ML) framework that judiciously considers features and their combinations, and apply state-of-the-art ML techniques for making context-aware 5G throughput predictions. Our work can be viewed as a feasibility study for building what we envisage as a dynamic 5G throughput map (akin to Google traffic map) that serves as a fundamental building block for future 5G-aware apps. [Goto download page]
    (4) SIGCOMM 2021 5G Measurement Data
    Please checkout the project Github page for details.
    (5) SIGCOMM 2022 5G Mobility Study Data
    Please checkout the project Github page for details.
    (6) PAM 2023 mmWave 5G Latency Data
    Please checkout the project Github page for details.
  • Educational Activities
    • Courses Related to the Project:
    Fall 2021: CSCI 8980 Topics in Mobile Computing
    Fall 2020: CSCI 4211 Introduction to Computer Networks
    Fall 2019: CSCI 4211 Introduction to Computer Networks
    Spring 2019: CSCI 8980 Topics in Mobile Computing
  • Outreach Activities
    • CHI 2023 Technical Talk Video
    PAM 2023 Technical Talk Video
    SIGCOMM 2022 Technical Talk Video
    CHI 2022 Technical Talk Video
    NSDI 2022 Technical Talk Video
    WWW 2021 Technical Talk Video
    MobiCom 2021 Technical Talk Video
    SIGCOMM 2021 Technical Talk Video
    Lumos5G ACM IMC 2020 Technical Talk Video
    Firefly USENIX ATC 2020 Technical Talk Video
    MPBond ACM MobiSys 2020 Technical Talk Video
    Flare ACM MobiCom 2018 Technical Talk Video
    Flare ACM MobiCom 2018 Demo Video 1, Video 2