A deterministic QoE formalization of user satisfaction demands (DQX)

Measuring the impact of technical variables, such as latency, bandwidth, or resources priority-access, on Quality-of-Experience (QoE) of various services demands an extensive feedback from end-users, when those variables change. Estimating QoE in a given scenario becomes harder, when non-technical variables, such as price, need to be considered in addition to technical ones. In any case, detailed feedback that correlates all variables affecting QoE is needed by end-users for each service separately. In this work a deterministic mathematical model (DQX) encapsulating user demands, service characteristics, and variable specifications is proposed to formalize the QoE calculation, considering one or multiple and diverse variables. The output of QoE functions presented here can be normalized such that results will be compatible with the five-point scale Mean Opinion Score (MOS), proposed by the ITU-T.

[1]  Rocky K. C. Chang,et al.  Measuring the quality of experience of HTTP video streaming , 2011, 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops.

[2]  Burkhard Stiller,et al.  Challenging the Monopoly of Mobile Termination Charges with an Auction-Based Charging and User-Centric System (AbaCUS) , 2013, 2013 Conference on Networked Systems.

[3]  Peter Reichl,et al.  On the fixpoint problem of QoE-based charging , 2012, 6th International ICST Conference on Performance Evaluation Methodologies and Tools.

[4]  Markus Fiedler,et al.  Quantification of Quality of Experience for Edge-Based Applications , 2007, ITC.

[5]  P. Reichl.  Charging for Quality-of-Experience : A New Paradigm for Pricing IP-based Service , 2006 .

[6]  Markus Fiedler,et al.  Quality of Experience-related differential equations and provisioning-delivery hysteresis , 2010 .

[7]  Burkhard Stiller,et al.  An automatic and on-demand MNO selection mechanism , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[8]  Kay Connelly,et al.  Toward total quality of experience: A QoE model in a communication ecosystem , 2012, IEEE Communications Magazine.

[9]  Mainak Chatterjee,et al.  Evaluating Quality of Experience for Streaming Video in Real Time , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[10]  Burkhard Stiller,et al.  Investigations of an SLA Support System for Cloud Computing (SLACC) , 2011, Prax. Inf.verarb. Kommun..

[11]  P. Hards,et al.  Mapping User Level QoS from a Single , 1998 .

[12]  Markus Fiedler,et al.  Testing the IQX Hypothesis for Exponential Interdependency between QoS and QoE of Voice Codecs iLBC and G.711 , 2008 .

[13]  Kuan-Ta Chen,et al.  Could Skype be more satisfying? a QoE-centric study of the FEC mechanism in an internet-scale VoIP system , 2010, IEEE Network.

[14]  Gabi Dreo Rodosek,et al.  Towards evaluating type of service related Quality-of-Experience on mobile networks , 2014, 2014 7th IFIP Wireless and Mobile Networking Conference (WMNC).

[15]  Markus Fiedler,et al.  A generic quantitative relationship between quality of experience and quality of service , 2010, IEEE Network.

[16]  Stephen Littlechild,et al.  ‘Mobile Termination Charges: Calling Party Pays versus Receiving Party Pays’(original and revised versions) , 2004 .

[17]  Michael Seufert,et al.  YOUQMON: a system for on-line monitoring of YouTube QoE in operational 3G networks , 2013, PERV.