SUMMARY
Due to complexity and delay constraints a usually significant amount of residual redundancy remains in the source samples after source coding. This residual redundancy can be exploited by iterative source-channel decoding for error concealment and quality improvements. One key design issue in joint source-channel (de-)coding is the index assignment. Besides conventional index assignments optimized index assignments have been developed, e.g., considering zeroth or first order a priori information of the source samples. However, in real-world scenarios it is unlikely that the amount of residual redundancy is constant over time and thus it may occur that the just deployed index assignment is suboptimal at times when the residual redundancy differs too much from the amount that it is optimized for. In this paper the performance of optimized index assignments is examined that consider first order a priori knowledge under such suboptimal conditions.