Prospect Theoretic Pricing For QoE Modeling In Wireless Multimedia Networking

One of the biggest challenges in wireless multimedia communications is to provide satisfactory Quality of Experience (QoE) to the users. Recently, numerous QoE maximization metrics and techniques have been proposed to jointly improve the network performance and user satisfaction. However, these methods are built upon postulates of Expected Utility Theorem (EUT). In this paper, we discuss the limitations of EUT in modeling QoE and explore the nuances in Prospect Theory (PT) such as asymmetrical s-shaped value function and reference point dependence to develop a prospect-theoretic QoE maximization framework by incorporating price in QoE model. An algorithm to determine the amount of data that users should purchase at any given cost such that their QoE is maximized, is also presented. As an exemplary scenario, we consider a simplified multimedia communication network with two users, where both users request the same multimedia content and aim to achieve the best possible QoE. Traditional EUT-based price-QoE model has been adopted for the first user, while the proposed PT-based prospect theoretic multimedia pricing QoE model has been used for the second user. Simulation studies conducted with H.265 multimedia codec data reveal that PT user achieved higher QoE in comparison to EUT user at a lower cost. Results also indicated that PT-based modeling can improve system throughput and network revenue.

[1]  Narayan B. Mandayam,et al.  When Users Interfere with Protocols: Prospect Theory in Wireless Networks using Random Access and Data Pricing as an Example , 2014, IEEE Transactions on Wireless Communications.

[2]  Mugen Peng,et al.  Resource Allocation Optimization for Delay-Sensitive Traffic in Fronthaul Constrained Cloud Radio Access Networks , 2014, IEEE Systems Journal.

[3]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[4]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[5]  Wei Wang,et al.  Price the QoE, Not the Data: SMP-Economic Resource Allocation in Wireless Multimedia Internet of Things , 2018, IEEE Communications Magazine.

[6]  Wei Wang,et al.  Stackelberg Game-Theoretic Spectrum Allocation for QoE-Centric Wireless Multimedia Communications , 2019, EDGE.

[7]  Wei Wang,et al.  Joint High Level QP and Low Level Power Control in NOMA/OMA Downlink Wireless Multimedia Communications , 2019, 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[8]  R. Hunt The subtlety of distinctiveness: What von Restorff really did , 1995, Psychonomic bulletin & review.

[9]  Chia-han Lee,et al.  Prospect theoretic user satisfaction in wireless communications networks , 2015, 2015 24th Wireless and Optical Communication Conference (WOCC).

[10]  David D. Clark,et al.  Tussle in cyberspace: defining tomorrow's Internet , 2002, IEEE/ACM Transactions on Networking.

[11]  Christos Verikoukis,et al.  QoE-Aware Resource Allocation for Profit Maximization Under User Satisfaction Guarantees in HetNets With Differentiated Services , 2019, IEEE Systems Journal.

[12]  Wei Wang,et al.  QoE-Sensitive Economic Pricing Model for Wireless Multimedia Communications Using Stackelberg Game , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).