Ricky K.P. Mok
ABSTRACT
HTTP video streaming, employed by most of the video-sharing websites, allows users to control the video playback using, for example, pausing and switching the bit rate. These user-viewing activities can be used to mitigate the temporal structure impairments of the video quality. On the other hand, other activities, such as mouse movement, do not help reduce the impairment level. In this paper, we have performed subjective experiments to analyze user-viewing activities and correlate them with network path performance and user quality of experience. The results show that network measurement alone may miss important information about user dissatisfaction with the video quality. Moreover, video impairments can trigger user-viewing activities, notably pausing and reducing the screen size. By including the pause events into the prediction model, we can increase its explanatory power.
- Endace DAG card. http://www.endace.com.Google Scholar
- IBM SPSS Statistics. http://www.spss.com.Google Scholar
- Adobe. Video Technology Center, Delivery: Progressive download. http://www.adobe.com/devnet/video/progressive.html.Google Scholar
- E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In Proc. ACM SIGIR, 2006. Google ScholarDigital Library
- S. Avallone, S. Guadagno, D. Emma, A. Pescapè, and G. Ventre. D-ITG Distributed internet traffic generator. International Conf. on Quantitative Evaluation of Systems, 0:316--317, 2004. Google ScholarDigital Library
- R. Brant. Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics, 46(4):1171--1178, 1990.Google ScholarCross Ref
- C. Chen, S.-Y. Lee, and H. W. Stevenson. Response style and cross-cultural comparisons of rating scales among East Asian and North American students. Psychological Science, 6(3):170--175, 1995.Google ScholarCross Ref
- C. E. Downing. System usage behavior as a proxy for user satisfaction: an empirical investigation. Information & Management, 35:p.203--216, 1999. Google ScholarDigital Library
- V. Gopalakrishnan, R. Jana, R. Knag, K. Ramakrishnan, D. Swayne, and V. Vaishampayan. Characterizing interactive behavior in a large-scale operational IPTV environment. In Proc. IEEE INFOCOM, 2010. Google ScholarDigital Library
- Y. Ito and S. Tasaka. Quantitative assessment of user-level QoS and its mapping. IEEE Trans. on Multimedia, 7 Issue 3:572--584, June 2005. Google ScholarDigital Library
- ITU-R Recommendation BT.500-12. "Methodology for the subjective assessment of the quality of television pictures", Sept 2009.Google Scholar
- ITU-T Recommendation P.911. "Subjective audiovisual quality assessment methods for multimedia applications" Dec 1998.Google Scholar
- T. Joachims. Optimizing search engines using clickthrough data. In Proc. ACM SIGKDD, 2002. Google ScholarDigital Library
- J. Klaue, B. Rathke, and A. Wolisz. EvalVid - A framework for video transmission and quality evaluation. In Computer Performance, volume 2794 of LNCS, pages 255--272. Springer, 2003.Google Scholar
- E. Kohler, R. Morris, B. Chen, J. Jannotti, and M. F. Kaashoek. The click modular router. ACM Trans. Comput. Syst., 18(3):263--297, 2000. Google ScholarDigital Library
- R. Mok, E. Chan, and R. Chang. Measuring the quality of experience of HTTP video streaming. In Proc. IEEE/IFIP IM (pre-conf.), 2011.Google ScholarCross Ref
- N. J. D. Nagelkerke. A note on a general definition of the coefficient of determination. Biometrika, 78 Issue 3:691--692, 1991.Google ScholarCross Ref
- A. Perkis, S. Munkeby, and O. Hillestad. A model for measuring quality of experience. In Proc. NORSIG, 2006.Google ScholarCross Ref
- F. Sani and J. Todman. Experimental Design and Statistics For Psychology: A First Course. Blackwell, 2006.Google Scholar
- M. Siller and J. Woods. Using an agent based platform to map quality of service to experience in conventional and active networks. IEE Proc. - Communications, 153(6):828--840, 2006.Google ScholarCross Ref
- M. Vilas, X. Paneda, R. Garcia, D. Melendi, and V. Garcia. User behavior analysis of a video-on-demand service with a wide variety of subjects and lengths. In Proc. EUROMICRO, 2005. Google ScholarDigital Library
- W. Wu, A. Arefin, R. Rivas, K. Nahrstedt, R. Sheppard, and Z. Yang. Quality of experience in distributed interactive multimedia environments: toward a theoretical framework. In Proc. ACM MM, 2009. Google ScholarDigital Library
- H. Yu, D. Zheng, B. Y. Zhao, and W. Zheng. Understanding user behavior in large-scale video-on-demand systems. SIGOPS Oper. Syst. Rev., 40:333--344, April 2006. Google ScholarDigital Library
Index Terms
- Inferring the QoE of HTTP video streaming from user-viewing activities
Recommendations
QoE measurement of mobile YouTube video streaming
MoViD '10: Proceedings of the 3rd workshop on Mobile video deliveryThe scope of this paper is the interdisciplinary measurement and modeling of Quality of Experience (QoE) related to mobile YouTube video streaming in Living Lab environment. The paper introduces the implementation of a QoE measurement framework on the ...
Enhanced QoE-oriented packet scheduling scheme for HTTP video streaming in LTE networks
MobiMedia '15: Proceedings of the 8th International Conference on Mobile Multimedia CommunicationsHypertext transfer protocol (HTTP) video streaming is a growing and increasingly important data traffic type in wireless and mobile networks. Experiments have shown that the quality of experience (QoE) of this service strongly depends on the number of ...
An SDN-aided low-latency live video streaming over HTTP
AbstractDynamic adaptive streaming over HTTP (DASH) is the crucial factor in the rapid penetration of over-the-top (OTT) service providers for on-demand video streaming. It can also be used for live video streaming by the OTT providers. The recent ...
Comments