Research Article
Towards the Future Data Market: Reward Optimization in Mobile Data Subsidization
@INPROCEEDINGS{10.1007/978-3-030-63941-9_13, author={Zehui Xiong and Jun Zhao and Jiawen Kang and Dusit Niyato and Ruilong Deng and Shengli Xie}, title={Towards the Future Data Market: Reward Optimization in Mobile Data Subsidization}, proceedings={6GN for Future Wireless Networks. Third EAI International Conference, 6GN 2020, Tianjin, China, August 15-16, 2020, Proceedings}, proceedings_a={6GN}, year={2021}, month={1}, keywords={Data subsidization Next-generation mobile data Network economics Game theory}, doi={10.1007/978-3-030-63941-9_13} }- Zehui Xiong
Jun Zhao
Jiawen Kang
Dusit Niyato
Ruilong Deng
Shengli Xie
Year: 2021
Towards the Future Data Market: Reward Optimization in Mobile Data Subsidization
6GN
Springer
DOI: 10.1007/978-3-030-63941-9_13
Abstract
Mobile data subsidization launched by network operators is a promising business model to provide some economic insights on the evolving direction of the 4G/5G and beyond mobile data market. The scheme allows content providers to partly subsidize mobile data consumption of mobile users in exchange for displaying a certain amount of advertisements. The users are motivated to access and consume more content without being concerned about overage charges, yielding higher revenue to the data subsidization ecosystem. For each content provider, how to provide appropriate data subsidization (reward) competing with others to earn more revenue and gain higher profit naturally becomes the key concern in such a ecosystem. In this paper, we adopt a hierarchical game approach to model the reward optimization process for the content providers. We formulate an Equilibrium Programs with Equilibrium Constraints (EPEC) problem to characterize the many-to-many interactions among multiple providers and multiple users. Considering the inherent high complexities of the EPEC problem, we propose to utilize the distributed Alternating Direction Method of Multipliers (ADMM) algorithm to obtain the optimum solutions with fast-convergence and decomposition properties of ADMM.
