Research Article
A Comprehensive Analysis of Video Service Quality on IQIYI from Large-Scale Data Sets
@INPROCEEDINGS{10.1007/978-3-319-78078-8_9, author={Yao Guo and Qiujian Lv and Fang Liu and Jie Yang and Zhe Gao}, title={A Comprehensive Analysis of Video Service Quality on IQIYI from Large-Scale Data Sets}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 13th International Conference, QShine 2017, Dalian, China, December 16 -17, 2017, Proceedings}, proceedings_a={QSHINE}, year={2018}, month={4}, keywords={IQIYI Apache Spark Video quality User’s experience FP-Growth algorithm}, doi={10.1007/978-3-319-78078-8_9} }- Yao Guo
Qiujian Lv
Fang Liu
Jie Yang
Zhe Gao
Year: 2018
A Comprehensive Analysis of Video Service Quality on IQIYI from Large-Scale Data Sets
QSHINE
Springer
DOI: 10.1007/978-3-319-78078-8_9
Abstract
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.