We design an algorithm for OFDM receivers operating in co-channel interference conditions, where the serving and interfering transmitters are synchronized in time. The channel estimation problem is formulated as one of sparse signal reconstruction using multiple measurement vectors. The proposed design harnesses the sparse common support of, respectively, the desired and interfering MIMO sub-channels by adopting a Bernoulli-Gaussian prior for the weights of the impulse responses of these sub-channels. Then, applying a variational Bayesian inference method we derive an algorithm that performs joint channel estimation, interference cancellation and decoding. Simulation results show how the performance of the proposed receiver depends on its knowledge of the interfering signals' modulation and code. When these are known, our receiver approaches the performance of a genie-aided receiver with perfect interference cancellation. Even with mismatched assumptions on the modulation, our proposed implementation still outperforms receivers which neglect the interference.