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dc.contributor.authorPham, Ngoc Nam
dc.contributor.authorTran, Huyen T. T.
dc.contributor.authorTruong, Cong Thang
dc.contributor.authorHOßFELD, TOBIAS
dc.contributor.authorSEUFERT, MICHAEL
dc.date.accessioned2024-06-10T05:41:04Z
dc.date.available2024-06-10T05:41:04Z
dc.date.issued2021-04
dc.identifier.urihttps://vinspace.edu.vn/handle/VIN/86
dc.description.abstractHTTP Adaptive Streaming has become the de facto choice for multimedia delivery. 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 article, 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 four important components of the cumulative quality model, namely the minimum window quality, the last window quality, the maximum 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. Our subjective dataset as well as the source code of the proposed model have been made publicly available at https://sites.google.com/site/huyenthithanhtran1191/cqmdatabase.en_US
dc.language.isoen_USen_US
dc.subjectCumulative qualityen_US
dc.subjectquality modelen_US
dc.subjectquality of experienceen_US
dc.subjectadaptive video streamingen_US
dc.titleCumulative Quality Modeling for HTTP Adaptive Streamingen_US
dc.typeArticleen_US


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  • Pham Ngoc Nam, PhD [21]
    Vice Dean, College of Engineering and Computer Science - Director, Electrical Engineering program, College of Engineering and Computer Science

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