This paper considers the end-to-end mean squared error distortion in reconstructing a memoryless proper-complex Gaussian source transmitted over a set of parallel block-fading Gaussian noise channels, where the fading gains are modeled as correlated Rayleigh distributed random variables with different average powers, thus resulting in asymmetric average received signal noise ratios (SNRs). The distortion exponent (i.e., how fast the average distortion decays to zero as the average received SNR increases) of several coding strategies based on separate source and channel coding is characterized. The definition of distortion exponent commonly used in the literature for SNR-symmetric channels is generalized to the case of SNR-asymmetric channels. It is shown that fading correlation degrades the achievable mean squared error distortion, but does not affect the distortion exponent in the analyzed achievable schemes. The logarithm of the determinant of the fading correlation matrix is found to be a proxy for measuring the performance degradation due to correlation as compared with the case of independent fading. The proposed framework allows one to study any number of correlated parallel channels, contrary to the most of the literature that restricts attention to two channels only, with the same received SNR and with independent fading. In particular, a scheme based on the multiple description coding with more than two descriptions is analyzed; it is shown that determining the distortion exponent in this setting reduces to solving a linear program, which can be done numerically very efficiently. The proposed methodology relies on combining the ideas from linear innovation sequences and properties of determinant of sub-matrices. Interestingly, it is found that SNR-asymmetry is beneficial for multiple description coding when the total average received SNR in decibel is held constant. Even in the SNR-symmetric case, asymmetry in the compression rates is shown to lead to a larger distortion exponent than symmetric rates.