Recent research results have seen the application of parallelizing techniques to high-level synthesis. In particular, the effect of speculative code transformations on mixed control-data flow designs has demonstrated effective results on schedule lengths. In this paper we first analyze the use of the control and data dependence graph as an intermediate representation that provides the possibility of extracting the maximum parallelism. Then we analyze the scheduling problem by formulating an approach based on Integer Linear Programming (ILP) to minimize the number of control steps given the amount of resources. We improve the already proposed ILP scheduling approaches by introducing a new conditional resource sharing constraint which is then extended to the case of speculative computation. The ILP formulation has been solved by using a Branch and Cut framework which provides better results than standard branch and bound techniques.