+7 (495) 957-77-43

article-2_4_2019

Извините, этот техт доступен только в “Американский Английский”. For the sake of viewer convenience, the content is shown below in the alternative language. You may click the link to switch the active language.

MODELS OF QOE ENSURING FOR OTT SERVICES

Vasiliy S. Elagin, SPbGUT, St. Petersburg, Russia, elagin.vas@gmail.com
Ilya A. Belozertsev, SPbGUT, St. Petersburg, Russia, ilya.belozercev@outlook.com
Anastasia V. Onufrienko, SPbGUT, St. Petersburg, Russia, anastasia.4991@mail.ru

Abstract
The 4G network is becoming commercially large-scale worldwide, and the industry has begun research on fifth-generation (5G) mobile technologies. All this will increase the variety of multimedia services, especially for over-the-Top (OTT) services. OTT services have already gained great popularity and contributed to a large consumption of traffic, which offers a load on operators. Management solution QoE for traditional multimedia services obsolete, which creates new problems in the aspects of the management of yo for suppliers of services. This article discusses the main models that contribute to improving the quality of OTT services. The main parameter for quality assessment was chosen QoE-Quality of Experience. An analysis was made of a number of factors that directly affect the assessment of QoE. The second part of the article deals with models that can provide the necessary level of quality for OTT services. These models were divided into three groups: traffic-based models, application-based models, and speed-based models. The main task of the study is to find optimal solutions to ensure the quality of OTT services.

Keywords: OTT Services, QoE, QoS, MOS, quality models.

References

1. Goldshtein B., EvaginV., Belozertsev I. (2018). About quality of OTT Services in LTE. Vestnik Sviazy. 07, 7, ðð. 9-12. (in Russian)
2. Elagin V.S., Goldshtein A.B., Onufrienko A.V., Zarubin A.A., Belozertsev I.A. (2018). Synchronization of delay for OTT services in LTE. 2018 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), Minsk, 2018, pp. 1-4.
3. Elagin V.S., Goldshtein B.S., Onufrienko A.V., Zarubin A.A., Savelieva A.A. (2018). The efficiency of the DPI system for identifying traffic and providing the quality of OTT services. 2018 Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 2018, pp. 1-5.
4. 3GPP TS 26.247, «Transparent End-to-End Packet Switched Streaming Service (PSS); Progressive Download and Dynamic Adaptive Streaming Over HTTP (3GP-DASH)».
5. Steven Latr, Nicolas Staelens, Pieter Simoens. (2008). On-line estimation of the QoE of progressive download services in multimedia access networks[C]. ICOMP 2008, Las Vegas, Nevada, USA. 2008, ðð. 14-17.
6. Ricky K.P. Mok, Edmond W.W. Chan, and Rocky K.C. Chang. (2011). Measuring the Quality of Experience of HTTP Video Streaming[C]. Integrated Network Management (IM), 2011 IFIP/IEEE International Symposium, Dublin. 2011, pp. 485-492.
7. Ricky K.P. Mok, Edmond W.W. Chan, and Rocky K.C. Chang. (2002). Inferring the QoE of HTTP Video Streaming from User-Viewing. Activities [C]. W-MUS [16] S. Mohamed and G. Rubino, «A Study of Real-time Packet Video Quality Using Random Neural Networks,» IEEE Trans. On Circuits and Systems for Video Tech., 2002, 12(12),  pp. 1071-1083.
8. International Telecommunication Union. Geneva. Methods for subjective determination of transmission quality. Report ITU TP.800, 1996.
9. Vaneet Aggarwal, Emir Halepovic, Prometheus: Toward Quality-of-Experience Estimation for Mobile Apps from Passive Network Measurements, ACM HotMobile’14, Santa Barbara, CA, USA, February 26-27, 2014.
10. Balachandran A., Sekar V., Akella A., Seshan S. et al. (2012). A quest for an Internet video Quality-of-Experience, metric. In ACM HotNets.
11. Maxim Claeys. (2014). Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client. IEEE communications letters. Vol. 18. No. 4, April 2014.
12. Mohamed S. and Rubino G. (2002). A Study of Real-time Packet Video Quality Using Random Neural Networks, IEEE Trans. On Circuits and Systems for Video Tech., 2002-12, 12(12), pp. 1071-1083.
13. VQEG, «Final report from the video quality experts group on the validation of objective models of video quality assessment».
14. Johan De Vriendt, Danny De Vleeschauwer. (2013). Model for estimating QoE of Video delivered using HTTP Adaptive Streaming[C], IFIP/IEEE IM, 2013.

Information about authors:
Vasiliy S. Elagin, associate Professor of the Department of Infocommunication systems of SPbGUT, St. Petersburg, Russia
Ilya A. Belozertsev, postgraduate, Department of Infocommunication systems of SPbGUT, St. Petersburg, Russia
Anastasia V. Onufrienko, postgraduate, Department of Infocommunication systems of SPbGUT, St. Petersburg, Russia