Abstract
WebRTC is an open source project which enables real-time communication within web browsers. It facilitates web-based multimedia applications, e.g. video conferencing and receives great interest from the academia. Nevertheless understanding of quality of experience (QoE) for the WebRTC video applications in wireless environment is still desired. For the QoE metric, we focus on the widely accepted video freezing event. We propose to identify a freezing event by comparing the interval of receiving time between two successive video frames, named F-Gap, with a threshold. To enable automatically tracking of video freezing, we modify the original WebRtc protocol to punch receiving timestamp on the frame overhead. Furthermore, we evaluate the correlation between video freezing and quality of service (QoS) in WiFi network based on experiments in typical indoor environment. We build a machine learning model to infer whether QoE is unacceptable or not in the next time window based on current QoS metrics. Experiments verify that the model has good accuracy and the QoE state is mainly relevant to quality metrics of Round-Trip Time, Link Quality and RSSI. This model is helpful to highlight the providers in system design and improve user experience via avoiding bad QoE in advance.
Supported by National Science Foundation of China under grant No. 61572071, 61271199 and 61301082.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
e2eSoft. http://www.e2esoft.cn/vcam/.
References
Sun, W., Qin, X.: End-to-end delay analysis of wechat video call service in live DC-HSPA+ network. In: Proceedings of International Conference on 6th Wireless Communications and Signal Processing (WCSP), Heifei, China, pp. 1–5. IEEE (2014)
Taheri, S., et al.: WebRTCbench: a benchmark for performance assessment of webRTC implementations. In: Proceedings of 13th Embedded Systems For Real-time Multimedia (ESTIMedia), Amsterdam, Netherlands, pp. 1–7. IEEE (2015)
De Cicco, L., Carlucci, G., Mascolo, S.: Experimental investigation of the Google congestion control for real-time flows. In: 1st Proceedings of the ACM SIGCOMM Workshop on Future Human-Centric Multimedia Networking, Hong Kong, China, pp. 21–26. ACM (2013)
Rodríguez, P., Cerviño, J., Trajkovska, I., Salvachúa, J.: Advanced videoconferencing based on WebRTC. In: Proceedings of 9th IADIS International Conferences Web Based Communities and Social Media and Collaborative Technologies, Lisbon, Portugal, pp. 180–184. IADIS (2012)
Jang-Jaccard, J., Nepal, S., Celler, B., Yan, B.: WebRTC-based video conferencing service for telehealth. Computing 98(1–2), 169–193 (2016)
Balachandran, A., Sekar, V., Akella, A., Seshan, S., Stoica, I., Zhang, H.: Developing a predictive model of quality of experience for internet video. ACM SIGCOMM Comput. Commun. Rev. 43(4), 339–350 (2013)
Balachandran, A., Sekar, V., Akella, A., Seshan, S., Stoica, I., Zhang, H.: A quest for an internet video quality-of-experience metric. In: 11th Proceedings of the ACM Workshop on Hot Topics in Networks, Seattle, WA, USA, pp. 97–102. ACM (2012)
Carullo, G., Tambasco, M., Di Mauro, M., Longo, M.: A performance evaluation of WebRTC over LTE. In: 12th Annual Conference on Wireless On-demand Network Systems and Services (WONS), Cortina d’Ampezzo, Italy, pp. 1–6. IEEE (2016)
Vucic, D., Skorin-Kapov, L.: The impact of mobile device factors on QoE for multi-party video conferencing via WebRTC. In: Proceedings of 13th International Conference on Telecommunications (ConTEL), Graz, Australia, pp. 1–8. IEEE (2015)
Dobrian, F., et al.: Understanding the impact of video quality on user engagement. ACM 41(4), 362–373 (2011)
Yu, C., Xu, Y., Liu, B., Liu, Y.: Can you see me now? A measurement study of mobile video calls. In: Proceedings IEEE 33rd INFOCOM, Toronto, Canada, pp. 1456–1464. IEEE (2014)
Joumblatt, D., Chandrashekar, J., Kveton, B., Taft, N., Teixeira, R.: Predicting user dissatisfaction with internet application performance at end-hosts. In: 32nd Proceedings IEEE INFOCOM, Turin, Italy, pp. 235–239. IEEE (2013)
STRATACONF Homepage. http://strataconf.com/strata2012/public/schedule/detail/22658. Accessed 4 Feb 2012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yan, S., Guo, Y., Chen, Y., Xie, F. (2019). Predicting Freezing of WebRTC Videos in WiFi Networks. In: Zheng, J., Xiang, W., Lorenz, P., Mao, S., Yan, F. (eds) Ad Hoc Networks. ADHOCNETS 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-05888-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-05888-3_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05887-6
Online ISBN: 978-3-030-05888-3
eBook Packages: Computer ScienceComputer Science (R0)