Transport Optimization for Streaming Systems

TranSys is designed to achieve high-performance interactive streaming services for high-resolution, low-latency transport. There are three sub-projects:

• Metis (Homepage). It provides an interpretation method for the deep learning-based networking systems, with a case study on the video streaming system.
• PiTree (Homepage). It provides a conversion method from heavyweight adaptive bit-rate algorithms into lightweight decision trees.
• PiTree Dataset (Homepage). It provides a dataset of network conditions that collected by us for the emulation of high-resolution video streaming.

Publications

• Zili Meng, Yaning Guo, Yixin Shen, Jing Chen, Chao Zhou, Minhu Wang, Jia Zhang, Mingwei Xu, Chen Sun, Hongxin Hu. Practically Deploying Heavyweight Adaptive Bitrate Algorithms With Teacher-Student Learning. IEEE/ACM Transactions on Networking (ToN) 2021.
• Zili Meng, Minhu Wang, Jiasong Bai, Mingwei Xu, Hongzi Mao, Hongxin Hu. Interpreting Deep Learning-Based Networking Systems. ACM SIGCOMM 2020.
• Zili Meng, Jing Chen, Yaning Guo, Chen Sun, Hongxin Hu, Mingwei Xu. PiTree: Practical Implementation of ABR Algorithms Using Decision Trees. ACM Multimedia 2019.

Contributing Members

(sorted by the first name)

• Chao Zhou (Kuaishou)
• Chen Sun (Tsinghua University)
• Hongxin Hu (University at Buffalo, SUNY)
• Hongzi Mao (Massachusetts Institute of Technology)
• Jia Zhang (Tsinghua University)
• Jiasong Bai (Tsinghua University)
• Jing Chen (Tsinghua University)
• Mingwei Xu (Tsinghua University)
• Minhu Wang (Tsinghua University)
• Yaning Guo (Tsinghua University)
• Yixin Shen (Tsinghua University)
• Zili Meng (Tsinghua University)

Acknowledgement

The TranSys project has been supported by the National Key R&D Program of China (2017YFB0801701) and National Science Foundation of China (61625203, 61832013, 61872426). Zili is also supported by a Microsoft Research Asia Fellowship.

Contact

For any questions, please send an email to zilim@ieee.org.