Today's mobile data applications aspire to deliver services to a user anywhere - anytime while fulfilling his Quality of Service (QoS) requirements. However, the success of the service delivery heavily relies on the QoS offered by the underlying networks. As the services operate in a heterogeneous networking environment, we argue that the generic information about the networks' offered-QoS may enable an anyhow mobile service delivery based on an intelligent (proactive) selection of 'any' network available in the user's context (location and time). Towards this direction, we develop a QoS-predictions service provider, which includes functionality for the acquisition of generic offered-QoS information and which, via a multidimensional processing and history-based reasoning, will provide predictions of the expected offered-QoS in a reliable and timely manner. We acquire the generic QoS-information from distributed mobile services' components quantitatively (actively and passively) measuring the application-level QoS, while the reasoning is based on statistical data mining and pattern recognition techniques.