Abstract
HTTP Adaptive Streaming has become the de facto choice for multimedia delivery nowadays. However, the quality of adaptive video streaming may fluctuate strongly during a session due to throughput fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this paper, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify three important components of the cumulative quality model, namely the minimum window quality, the last window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation.
Database
Our database has been made available to the public: [Database]
Source code of CQM model: https://github.com/TranHuyen1191/CQM
Acknowledgments
If you use this source code in your research, please cite the references below:
Huyen T. T. Tran, Nam Pham Ngoc, Tobias Hoßfeld, Michael Seufert, Truong Cong Thang, "Cumulative Quality Modeling for HTTP Adaptive Streaming," in https://arxiv.org/abs/1909.02772, submitted to ACM Transactions on Multimedia Computing Communications and Applications, accepted.