In the framework of mobile augmented reality, a video stream is sent to the user with the help of a wireless communication link. To guarantee an efficient transmission, the video stream rate is controlled by adapting the encoding parameters such as to follow a given bandwidth. The rate can be reduced by reducing the frame rate and/or by choosing a higher compression factor for the video stream. These parameter modifications impact both the level of detail and the fluidity perceived by the user, and thus his/her subjective appreciation. The experience perceived by the user also depends on the context. During a rapid head motion, the notion of fluidity is more important than for a fixed head position. We propose an end-to-end adaptation scheme which enables the encoding of parameters such as to provide the best experience for the user regarding the dynamical context. For example, when the user moves quickly his/her head, the frame is compressed more to increase the frame rate and hence achieve a better perception of the motion. The lack of direct measurement for the subjective user experience is addressed with the design of objective metrics and a generic model to predict the user quality of experience in real time. A rate control strategy based on a systems approach is deployed to manage the multiple encoding parameters which control the stream rate. The encoder is modeled in an abstract manner as a single-variable linear system, where the content variation is taken as a perturbation. A stable and efficient controller is designed for the abstract model of the encoder. To implement the designed controller, the parameter combinations for the real encoder corresponding to the single input of the abstract model should be determined. A new one-pass algorithm determines this correspondence in real time based on a mapping method. Then, the proposed contextual adaptation enables to get the encoding parameter combination that maximizes the quality of experience using an appropriate model. Finally, the global adaptation scheme combines the rate control, the mapping method and the contextual adaptation for real-time implementation. Simulation and experiments illustrate the approach and the global adaptation scheme is validated through different scenarios.