The constant pursuit for performance in Internet communications leads to network protocols and their mechanisms getting more and more involved and hence complex. Even seemingly simple mechanisms such as resource prioritization in HTTP/2 turn out to have very complex parameter interdependencies. Today’s protocol performance does not only depend on its configuration on client- and server-side, but also on the configuration of the other protocol layers in the respective network stack, rendering protocol tailoring into a vertical and horizontal cross-layer optimization problem. Traditional cross-layer optimization work analyses the influences and dependencies manually and finds optimization strategies. However, the increasing complexities let the parameter space grow, such that it is hard to find appropriate strategies. Thus, the parameter space is typically reduced, which, however, limits the holistic view and can hence reduce the effective performance gains. E.g., for resource prioritization, the strategies achieve only mediocre speedups in comparison to their complexities. Hence, the mechanism is mainly left unused. This effect is not exclusive to HTTP/2, but occurs in many similar cases. Techniques from the area of machine learning can handle big parameter spaces. They are already used for protocol optimization and the approaches using these techniques give insights on how to use them, emphasizing that simply applying them is not the right way, as domain knowledge is needed for validation. However, the approaches view the layers agnostically ignoring the cross-layer component. Hence, it is unknown how to apply these techniques in the realm of cross-layer optimization. We propose to create a methodology for cross-layer protocol optimization involving machine learning, where we answer how to use it for capturing the complex interdependencies of the Internet protocol landscape. Our goals are not limited to only optimizing network protocols. Instead, insights should be given in regard to which approaches are suitable, how to constrain them, what decisions they make and how to feed the gained strategies back into protocol development.