  The column Hamming distance of a convolutional code determines the error
correction capability when streaming over a class of packet erasure channels.
We introduce a metric known as the column sum rank, that parallels column
Hamming distance when streaming over a network with link failures. We prove
rank analogues of several known column Hamming distance properties and
introduce a new family of convolutional codes that maximize the column sum rank
up to the code memory. Our construction involves finding a class of
super-regular matrices that preserve this property after multiplication with
non-singular block diagonal matrices in the ground field.
