The min-cut, max-flow theorem states that a source node can send a commodity through a network to a sink node at the rate determined by the flow of the min-cut separating the source and the sink. Recently it has been shown that by liner re-encoding at nodes in communications networks, the min-cut rate can be also achieved in multicasting to several sinks. In this paper we discuss connections between such coding schemes and convolutional codes. We propose a method to simplify the convolutionalencoder design that is based on a subtree decompositionof the network line graph, describe the structure of the associated matrices, investigate methods to reduce decoding complexity and discuss possible binary implementation.