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An unprecedented increase in the mobile data traffic volume has been recently reported due to the extensive use of smartphones, tablets and laptops. Moreover, predictions say that this increase is going to be yet more pronounced in the next 3-4 years. This is a major concern for mobile network operators, who are forced to often operate very close to (or even beyond) their capacity limits. Recently, different solutions have been proposed to overcome this problem. The deployment of additional infrastructure, the use of more advanced technologies (LTE), or offloading some traffic through Femtocells and WiFi are some of the solutions. Out of these, WiFi presents some key advantages such as its already widespread deployment and low cost. While the benefits to operators have already been documented, with considerable amounts of traffic already switched over to WiFi, it is less clear how much and under what conditions the user gains as well.To this end, in this paper we propose a queueing analytic model that can be used to understand the performance improvements achievable by WiFi-based data offloading, as a function of WiFi availability and performance, and user mobility and traffic load. We validate our theory against simulations for realistic data and scenarios, and provide some initial insights as to the offloading gains expected in practice.

Operators have recently resorted to WiFi offloading to deal with increasing data demand and induced congestion. Researchers have further suggested the use of "delayed offloading": if no WiFi connection is available, (some) traffic can be delayed up to a given deadline, or until WiFi becomes available. Nevertheless, there is no clear consensus as to the benefits of delayed offloading, with a couple of recent experimental studies largely diverging in their conclusions. Nor is it clear how these benefits depend on network characteristics (e.g. WiFi availability), user traffic load, etc. In this paper, we propose a queueing analytic model for delayed offloading, and derive the mean delay, offloading efficiency, and other metrics of interest, as a function of the user's "patience", and key network parameters. We validate the accuracy of our results using a range of realistic scenarios, and use these expressions to show how to optimally choose deadlines.

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