Many techniques exist for adapting videos to satisfy heterogeneous resource conditions or user preferences, whereas selection of the best adaptation operation among various choices usually is either ad hoc or inefficient. To provide a systematic solution, we present a conceptual framework based on utility function (UF), which models video entity, adaptation, resource, utility, and the relations among them. In order to support real-time video adaptation, we present a content-based statistical paradigm to facilitate the prediction of UF for real-time transcoding of live videos. Instead of modelling the UF through analytical models, as in the conventional rate-distortion framework, the proposed approach formulates the prediction as a classification and regression problem. Each video clip is classified into one of distinctive categories and then local regression is used to accurately predict the utility value. Our extensive experiment results based on MPEG-4 transcoding demonstrate that the proposed method achieves very promising performance-up to 89% accuracy in choosing the optimal transcoding operation (in both,spatial and temporal dimensions) with the highest quality over a diverse range of target bit rates.