Multi-State Video Coding (MSVC) is a multiple description scheme based on frame-wise splitting of the video sequence into two or more subsequences. Each subsequence is encoded separately to generate descriptions which can be decoded independently. Due to subsequence splitting the prediction gain decreases. but since reconstruction capabilities improves, error resilience of the system increases. Our focus is on Multi-State Video Coding with unbalanced quantized descriptions, which is particularly interesting for video streaming applications over heterogeneous networks where path diversity is used and transmission channels have varying transmission characteristics. The total bitrate is kept constant while the subsequences are quantized with different step sizes depending on the sequence as well as on the transmission conditions. Our goal is to figure out under which transmission conditions unbalanced bitstreams lead to good system performance in terms of the average reconstructed PSNR. Besides, we investigate the effects of intra-coding on the error resilience of the system and show that the sequence characteristics, and in particular the degree of motion in the sequence, have an important impact on the decoding performance. Finally, we propose a distortion model that is the core of an optimized rate allocation strategy, which is dependent on the network characteristics and status as well as on the video sequence characteristics.