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This paper presents a packet loss recovery method that uses an incomplete secondary encoding based on scalar quantization as redundancy. The method is redundancy bit rate scalable and allows an adaptation to varying loss scenarios and a varying packeting strategy. The recovery is performed by minimum mean squared error estimation incorporating a statistical model for the quantizers to facilitate real-time adaptation. A bit allocation algorithm is proposed that extends 'reverse water filling' to the problem of scalar encoding dependent variables for a decoder with a final estimation stage and available side information. We apply the method to the encoding of line-spectral frequencies (LSFs), which are commonly used in speech coding, illustrating the good performance of the method.