Measuring the quality of experience of HTTP video streaming

By Ricky K. P. Mok, Edmond W. W. Chan, Rocky K. C. Chang. IFIP/IEEE Symposium on Integrated Network Management, 2011

527 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/02160860e66ac561530f7339ad343781a055147b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Thomas Zinner
2. Phuoc Tran-Gia
3. Rocky K. C. Chang
4. Michael Seufert
5. Tobias Hossfeld
6. Yanjiao Chen
7. Qian Zhang
8. Matthias Hirth

In Computer Vision and Pattern Recognition

1. Yanjiao Chen
2. Xiapu Luo
3. Qian Zhang
4. Kaishun Wu
5. Zeina Sinno
6. A. Bovik

In Sociology and Political Science

1. Yanjiao Chen

Citations in the corpus

• QDASH: a QoE-aware DASH system (2012, background): “Although intermediate levels can smooth out the picture quality change, rebuffering events also reduce the QoE [29].”
• Quantification of YouTube QoE via Crowdsourcing (2011, background): “Because of the costs and time demands posed by laboratory tests, only a limited set of influence factors can be tested per test session.”
• From QoS to QoE: A Tutorial on Video Quality Assessment (2015, background): “Two major user-activity metrics, number of pause event and number of screen size reducing event, are put into the logistic regression model, along with the three APM metrics.”