This paper presents a planning framework for jumping over obstacles with quadruped robots. The framework accomplishes planning via a structured predictive control strategy that combines the use of heterogeneous simplified models over different prediction time scales. A receding multi-horizon predictive controller coordinates the approach before the jump using a kinematic point-mass model. Consideration of the optimal value function over different planning horizons enables the system to select an appropriate number of steps to take before jumping. The jumping motion is then tailored to the sensed obstacle by solving a nonlinear trajectory optimization problem. The solution of this problem online is enabled by exploiting the analyticity of the flow map for a planar bounding template model under polynomial inputs. By planning with this combination of models, MIT Cheetah 2 is shown to autonomously jump over obstacles up to 40 cm in height during high-speed bounding. Untethered results showcase the ability of the method to automatically adapt to obstacles of different heights and placements in a single trial.