Economics of Mobile Data Trading Market

To exploit users' heterogeneous data demands, several mobile network operators worldwide have launched the mobile data trading markets, where users can trade mobile data quota with each other. In this work, we aim to understand the users' optimal trading decisions and the operator's revenue maximizing strategy. We model the interactions between the mobile operator and the users as a two-stage Stackelberg game. In Stage I, the operator chooses the operation fee imposed on sellers to maximize its revenue. In Stage II, each user decides whether to be a seller or a buyer and optimizes the corresponding trading price and quantity. We derive the closed-form expression of the unique Nash equilibrium (NE) in Stage II in closed-form, and prove that the users' decisions can converge to the NE through distributed best response updates. We show that at the NE, different types of sellers and buyers should propose the same price such that the total demand matches the total supply. We further show that the Stage I operation fee optimization problem is convex, and derive the optimal operation fee in closed-form. Our analysis and numerical results show that the users who have less uncertainty of their data usages can benefit more from data trading. We also show that an operation fee that is too high hurts both the users' payoffs and the operator's revenue.