  Differential linear network coding (DLNC) is a precoding scheme for
information transmission over random linear networks. By using differential
encoding and decoding, the conventional approach of lifting, required for
inherent channel sounding, can be omitted and in turn higher transmission rates
are supported. However, the scheme is sensitive to variations in the network
topology. In this paper, we derive an extended DLNC channel model which
includes slow network changes. Based on this, we propose and analyze a suitable
channel coding scheme matched to the situation at hand using rank-metric
convolutional codes.
