  Mobile data traffic has been steadily rising in the past years. This has
generated a significant interest in the deployment of incentive mechanisms to
reduce peak-time congestion. Typically, the design of these mechanisms requires
information about user demand and sensitivity to prices. Such information is
naturally imperfect. In this paper, we propose a \emph{fixed-budget rebate
mechanism} that gives each user a reward proportional to his percentage
contribution to the aggregate reduction in peak time demand. For comparison, we
also study a time-of-day pricing mechanism that gives each user a fixed reward
per unit reduction of his peak-time demand. To evaluate the two mechanisms, we
introduce a game-theoretic model that captures the \emph{public good} nature of
decongestion. For each mechanism, we demonstrate that the socially optimal
level of decongestion is achievable for a specific choice of the mechanism's
parameter. We then investigate how imperfect information about user demand
affects the mechanisms' effectiveness. From our results, the fixed-budget
rebate pricing is more robust when the users' sensitivity to congestion is
"sufficiently" convex. This feature of the fixed-budget rebate mechanism is
attractive for many situations of interest and is driven by its closed-loop
property, i.e., the unit reward decreases as the peak-time demand decreases.
