Reward Optimization for Content Providers With Mobile Data Subsidization: A Hierarchical Game Approach

Mobile data subsidization launched by mobile network operators is a promising business model to provide economic benefits for the mobile data market and beyond. It allows content providers to partly subsidize mobile data consumption of mobile users in exchange for displaying a certain amount of advertisements. From a content provider perspective, it is of great interest to determine the optimal strategy for offering appropriate data subsidization (reward) in order to compete against others to earn more revenue and gain higher profit. In this paper, we take a hierarchical game approach to model the reward optimization process for the content providers. To analyze the relationship between the provider and the user, we first focus on the one-to-one interaction in a single-provider single-user system, and formulate a Mathematical Program with Equilibrium Constraints (MPEC). We apply the backward induction to solve the MPEC problem and prove the existence and uniqueness of the Stackelberg equilibrium. We then formulate an Equilibrium Program with Equilibrium Constraints (EPEC) to characterize the many-to-many interactions among multiple providers and multiple users. Considering the inherent high complexity of the EPEC problem, we utilize the distributed Alternating Direction Method of Multipliers (ADMM) algorithm to obtain the optimum solutions with fast-convergence and decomposition properties of ADMM.

[1]  Zhu Han,et al.  Distributed Resource Allocation for Data Center Networks: A Hierarchical Game Approach , 2020, IEEE Transactions on Cloud Computing.

[2]  Zhu Han,et al.  Colonel Blotto Games in Network Systems: Models, Strategies, and Applications , 2020, IEEE Transactions on Network Science and Engineering.

[3]  Lingyang Song,et al.  Equilibrium Problems With Equilibrium Constraints Analysis for Power Control and User Scheduling in NOMA Networks , 2020, IEEE Transactions on Vehicular Technology.

[4]  Dusit Niyato,et al.  Cloud/Edge Computing Service Management in Blockchain Networks: Multi-Leader Multi-Follower Game-Based ADMM for Pricing , 2020, IEEE Transactions on Services Computing.

[5]  Yang Zhang,et al.  A Stackelberg Game Approach for Sponsored Content Management in Mobile Data Market With Network Effects , 2020, IEEE Internet of Things Journal.

[6]  Zhu Han,et al.  Information Trading in Internet of Things for Smart Cities: A Market-Oriented Analysis , 2020, IEEE Network.

[7]  H. Vincent Poor,et al.  A Game-Theoretic Analysis for Complementary and Substitutable IoT Services Delivery With Externalities , 2020, IEEE Transactions on Communications.

[8]  Zhu Han,et al.  Dynamic Pricing for Revenue Maximization in Mobile Social Data Market With Network Effects , 2018, IEEE Transactions on Wireless Communications.

[9]  Mianxiong Dong,et al.  Cooperative Inter-Domain Cache Sharing for Information-Centric Networking via a Bargaining Game Approach , 2019, IEEE Transactions on Network Science and Engineering.

[10]  D. Manjunath,et al.  Sponsored Data with ISP Competition , 2019, 2019 31st International Teletraffic Congress (ITC 31).

[11]  Mohsen Guizani,et al.  An Incentive Mechanism Design for Socially Aware Crowdsensing Services with Incomplete Information , 2019, IEEE Communications Magazine.

[12]  Dusit Niyato,et al.  Deep Reinforcement Learning for Mobile 5G and Beyond: Fundamentals, Applications, and Challenges , 2019, IEEE Vehicular Technology Magazine.

[13]  Zhu Han,et al.  Joint Sponsored and Edge Caching Content Service Market: A Game-Theoretic Approach , 2019, IEEE Transactions on Wireless Communications.

[14]  Zhu Han,et al.  VLC and D2D Heterogeneous Network Optimization: A Reinforcement Learning Approach Based on Equilibrium Problems With Equilibrium Constraints , 2019, IEEE Transactions on Wireless Communications.

[15]  Jianwei Huang,et al.  Exploring Time Flexibility in Wireless Data Plans , 2018, IEEE Transactions on Mobile Computing.

[16]  Jun Luo,et al.  A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing , 2018, IEEE Transactions on Wireless Communications.

[17]  Zhu Han,et al.  A Hierarchical Game with Strategy Evolution for Mobile Sponsored Content/Service Markets , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[18]  Ursula Challita,et al.  Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial , 2017, IEEE Communications Surveys & Tutorials.

[19]  Mianxiong Dong,et al.  SEER-MCache: A Prefetchable Memory Object Caching System for IoT Real-Time Data Processing , 2018, IEEE Internet of Things Journal.

[20]  Zhu Han,et al.  Game Theory for Big Data Processing: Multileader Multifollower Game-Based ADMM , 2018, IEEE Transactions on Signal Processing.

[21]  Haitian Pang,et al.  Joint Sponsor Scheduling in Cellular and Edge Caching Networks for Mobile Video Delivery , 2018, IEEE Transactions on Multimedia.

[22]  Mianxiong Dong,et al.  QUOIN: Incentive Mechanisms for Crowd Sensing Networks , 2018, IEEE Network.

[23]  Zhu Han,et al.  Game Theoretic Approaches to Massive Data Processing in Wireless Networks , 2017, IEEE Wireless Communications.

[24]  Dusit Niyato,et al.  Competition and cooperation analysis for data sponsored market: A network effects model , 2017, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[25]  Dusit Niyato,et al.  Economic Analysis of Network Effects on Sponsored Content: A Hierarchical Game Theoretic Approach , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[26]  Zhu Han,et al.  Large-Scale Fog Computing Optimization Using Equilibrium Problem with Equilibrium Constraints , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[27]  Karthikeyan Sundaresan,et al.  Economics of Quality Sponsored Data in Non-Neutral Networks , 2015, IEEE/ACM Transactions on Networking.

[28]  Derrick Wing Kwan Ng,et al.  Smart Data Pricing in 5G Systems , 2017 .

[29]  Yue Jin,et al.  A truthful pricing mechanism for sponsored content in wireless networks , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[30]  Dan Wang,et al.  TDS: Time-dependent sponsored data plan for wireless data traffic market , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[31]  Richard T. B. Ma Subsidization Competition: Vitalizing the Neutral Internet , 2014, IEEE/ACM Transactions on Networking.

[32]  Dan Wang,et al.  Sponsored Data Plan , 2015, SIGMETRICS.

[33]  Sangtae Ha,et al.  Sponsoring mobile data: An economic analysis of the impact on users and content providers , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[34]  Zhi-Quan Luo,et al.  Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems , 2014, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[35]  Dan Wang,et al.  Sponsoring content: Motivation and pitfalls for content service providers , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[36]  K. J. Ray Liu,et al.  Optimal Pricing Strategy for Operators in Cognitive Femtocell Networks , 2014, IEEE Transactions on Wireless Communications.

[37]  K. J. Ray Liu,et al.  Dynamic Chinese Restaurant Game: Theory and Application to Cognitive Radio Networks , 2014, IEEE Transactions on Wireless Communications.

[38]  Qing Ling,et al.  On the Linear Convergence of the ADMM in Decentralized Consensus Optimization , 2013, IEEE Transactions on Signal Processing.

[39]  Qiong Wang,et al.  Economic models of sponsored content in wireless networks with uncertain demand , 2013, 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[40]  Michal Kočvara,et al.  Nonsmooth approach to optimization problems with equilibrium constraints : theory, applications, and numerical results , 1998 .