The Regressive QoE Model for VoLTE

LTE is a 3GPP full IP-based standard for wireless transmission systems. Due to its characteristic of high throughput, an efficient end-to-end QoS treatment is needed in order to guarantee good quality perceived by end users. LTE provides a native QoS-aware mechanism for end-to-end service delivering based on EPS bearer and QCI. Customers today may choose mobile services among different competing network operators according to their experienced quality of the services. The network operators must be able to react quickly to quality issues to offer more satisfied services before the users grumble. It is essential to establish a relationship between QoE and QoS reacting to the network performance. The voice service delivered by the LTE system is a kind of Voice over IP (VoIP) service with no QoS-guaranteed mechanism. LTE Standard specifies the QoS, but doesn't define the QoS/QoE relationship. This research identifies the QoS and QoE parameters, named respectively KPI and KQI for the VoLTE service, and then analyzes the QoS/QoE mathematical relationship. The main contribution of this study relies on the possibility to predict the QoE level through the functional relation with QoS. We propose the linear regression technique to derive objective voice quality metric (MOS) through subjective tests and show the correlative causality of KPIs and MOS.

[1]  Rafik Goubran,et al.  Assessment of effects of packet loss on speech quality in VoIP , 2003, The 2nd IEEE Internatioal Workshop on Haptic, Audio and Visual Environments and Their Applications, 2003. HAVE 2003. Proceedings..

[2]  Sonia Forconi,et al.  QoS KPI and QoE KQI Relationship for LTE VIdeo Streaming and VoLTE Services , 2015, 2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies.

[3]  J. Yu,et al.  Call Admission Control and Traffic Engineering of VoIP , 2007, 2007 Second International Conference on Digital Telecommunications (ICDT'07).

[4]  Ocevcic Hrvoje,et al.  Adapted E-model and dynamic adjustment of VoIP parameters , 2011, Proceedings of the 11th International Conference on Telecommunications.

[5]  Zhuoqing Morley Mao,et al.  Performance Characterization and Call Reliability Diagnosis Support for Voice over LTE , 2015, MobiCom.

[6]  Chin-Laung Lei,et al.  Radar chart: Scanning for high QoE in QoS dimensions , 2010, 2010 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR 2010).

[7]  John Fitzpatrick.  An E-Model based adaptation algorithm for AMR voice calls , 2011, 2011 IFIP Wireless Days (WD).

[8]  Fernando A. Kuipers,et al.  Techniques for Measuring Quality of Experience , 2010, WWIC.

[9]  Lea Skorin-Kapov,et al.  Survey and Challenges of QoE Management Issues in Wireless Networks , 2013, J. Comput. Networks Commun..

[10]  Patrick Bauer,et al.  On speech quality assessment of artificial bandwidth extension , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Shenghui Zhao,et al.  An improved speech playout buffering algorithm based on a new version of E-Model in VoIP , 2008, 2008 Third International Conference on Communications and Networking in China.

[12]  Peter J. Radcliffe,et al.  Dynamic QoS and Network Control for Commercial VoIP Systems in Future Heterogeneous Networks , 2008, 2008 Tenth IEEE International Symposium on Multimedia.

[13]  Arkady B. Zaslavsky,et al.  QoE Modelling, Measurement and Prediction: A Review , 2014, ArXiv.

[14]  Paavo Alku,et al.  A subjective listening test of six different artificial bandwidth extension approaches in English, Chinese, German, and Korean , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  J. Beerends,et al.  Perceptual Objective Listening Quality Assessment ( POLQA ) , The Third Generation ITU-T Standard for End-to-End Speech Quality Measurement Part II – Perceptual Model , 2013 .

[16]  Pedro A. Amado Assunção,et al.  Quality model for monitoring QoE in VoIP services , 2011, 2011 IEEE EUROCON - International Conference on Computer as a Tool.

[17]  Amandeep,et al.  A Survey on Quality of Service in LTE Networks , 2015 .

[18]  Hang Nguyen,et al.  A dynamic rate adaptation algorithm using WB E-model for voice traffic over LTE network , 2016, 2016 Wireless Days (WD).

[19]  Miroslav Voznak,et al.  E-MODEL MOS ESTIMATE IMPROVEMENT THROUGH JITTER BUFFER PACKET LOSS MODELLING , 2011 .

[20]  Adlen Ksentini,et al.  A_PSQA: PESQ-like non-intrusive tool for QoE prediction in VoIP services , 2012, 2012 IEEE International Conference on Communications (ICC).

[21]  Alexander Raake,et al.  Quality of Experience: Advanced Concepts, Applications and Methods , 2014 .

[22]  Therdpong Daengsi,et al.  QoE modeling: A simplified e-model enhancement using subjective MOS estimation model , 2015, 2015 Seventh International Conference on Ubiquitous and Future Networks.

[23]  Christian Alberto Rodríguez García.  VoIP Jitter in 3GPP Long Term Evolution Networks -Edición Única , 2009 .

[24]  Kamaljit I. Lakhtaria.  Enhancing QoS and QoE in IMS Enabled Next Generation Networks , 2009, 2009 First International Conference on Networks & Communications.

[25]  Conor Ryan,et al.  A Methodology for Deriving VoIP Equipment Impairment Factors for a Mixed NB/WB Context , 2008, IEEE Transactions on Multimedia.

[26]  Noel Crespi.  Toward Total Quality of Experience : A QoE Model in a Communication Ecosystem , .

[27]  Rafik A. Goubran,et al.  Speech quality prediction in VoIP using the extended E-model , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).