A Comprehensive Analysis of Video Service Quality on IQIYI from Large-Scale Data Sets

  • Yao GuoEmail author
  • Qiujian Lv
  • Fang Liu
  • Jie Yang
  • Zhe Gao
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 234)


With the proliferation of online video, measuring the quality of the video service has become a vital aspect for improving user’s experience. Recent work shows that measurable quality metrics such as buffering, bitrate, and video resolutions impact user’s experience, but none of them reveal the real relationships between these metrics and user’s actual experience. This paper attempts to solve the problem above. We use IQIYI as the sample, and our large-scale dataset consists of 7 days real Internet traffic in a northern city of China. We quantify user’s experience at per-video level (or view). Using Apache Spark, we extract some video events and calculate several quality metrics. In order to investigate the relationship between the metrics and user’s experience, we use the FP-Growth algorithm to mining the implicit association rules and get some interesting results.


IQIYI Apache Spark Video quality User’s experience FP-Growth algorithm 



This work is supported in part by the National Natural Science Foundation of China (61671078, 61701031), Director Funds of Beijing Key Laboratory of Network System Architecture and Convergence (2017BKL-NSAC-ZJ-06), and 111 Project of China (B08004, B17007). This work is conducted on the platform of Center for Data Science of Beijing University of Posts and Telecommunications.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Yao Guo
    • 1
    Email author
  • Qiujian Lv
    • 2
  • Fang Liu
    • 1
  • Jie Yang
    • 1
  • Zhe Gao
    • 3
  1. 1.Beijing Laboratory of Advanced Information Networks, Beijing Key Laboratory of Network System Architecture and Convergence, Center for Data ScienceBUPTBeijingChina
  2. 2.Institute of Information Engineering, Chinese Academy of SciencesBeijingChina
  3. 3.Technology Research InstituteAisino CorporationBeijingChina

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