Economic Viability of Data Trading with Rollover

Mobile Network Operators (MNOs) are providing more flexible wireless data services to attract subscribers and increase revenues. For example, the data trading market enables user-flexibility by allowing users to sell leftover data to or buy extra data from each other. The rollover mechanism enables time-flexibility by allowing a user to utilize his own leftover data from the previous month in the current month. In this paper, we investigate the economic viability of offering the data trading market together with the rollover mechanism, to gain a deeper understanding of the interrelationship between the user-flexibility and the time-flexibility. We formulate the interactions between the MNO and mobile users as a multi-slot dynamic game. Specifically, in each time slot (e.g., every day), the MNO first determines the selling and buying prices with the goal of revenue maximization, then each user decides his trading action (by solving a dynamic programming problem) to maximize his long-term payoff. Due to the availability of monthly data rollover, a user's daily trading decision corresponds to a dynamic programming problem with two time scales (i.e., day-to-day and month-to-month). Our analysis reveals an optimal trading policy with a target interval structure, specified by a buy-up-to threshold and a sell-down-to threshold in each time slot. Moreover, we show that the rollover mechanism makes users sell less and buy more data given the same trading prices, hence it increases the total demand while decreasing the total supply in the data trading market. Finally, numerical results based on real-world data unveil that the time-flexible rollover mechanism plays a positive role in the user-flexible data trading market, increasing the MNO's revenue by 25% and all users' payoff by 17% on average.Comment: IEEE INFOCOM 201

A Novel Mobile Data Contract Design with Time Flexibility

In conventional mobile data plans, the data is associated with a fixed period (e.g., one month) and the unused data will be cleared at the end of each period. To take advantage of consumers' heterogeneous demands across different periods and meanwhile to provide more time flexibility, some mobile data service providers (SP) have offered data plans with different lengths of period. In this paper, we consider the data plan design problem for a single SP, who provides data plans with different lengths of period for consumers with different characteristics of data demands. We propose a contract-theoretic approach, wherein the SP offers a period-price data plan contract which consists of a set of period and price combinations, indicating the prices for data with different periods. We study the optimal data plan contract designs under two different models: discrete and continuous consumer-type models, depending on whether the consumer type is discrete or continuous. In the former model, each type of consumers are assigned with a specific period-price combination. In the latter model, the consumers are first categorized into a finite number of groups, and each group of consumers (possibly with different types) are assigned with a specific period-price combination. We systematically analyze the incentive compatibility (IC) constraint and individual rationality (IR) constraint, which ensure each consumer to choose the data plan with the period-price combination intended for his type. We further derive the optimal contract that maximizes the SP's expected profit, meanwhile satisfying the IC and IR constraints of consumers. Our numerical results show that the proposed optimal contract can increase the SP's profit by 35%, comparing with the conventional fixed monthly-period data plan.Comment: This manuscript serves as the online technical report for the paper published in IEEE Transactions on Mobile Computin