In this paper, the optimal source model for the independent vector analysis (IVA) algorithm towards maximizing the output signal-to-interference ratio (SIR) is mathematically derived, and the corresponding optimal weighted covariance matrix is proved to be the covariance matrix of interference signals. A new algorithm framework called minimum variance IVA (MVIVA) is further proposed, where the deep neural network-based estimation of the interference covariance matrix is combined with the IVA-based estimation of the demixing matrix. Experimental results show the superiority of the proposed source model, and the MVIVA algorithm outperforms the original IVA algorithm by 9.6 dB in SIR and 5.8 dB in signal-to-distortion ration (SDR) on average.