  There are two fundamentally different fronthaul techniques in the downlink
communication of cloud radio access network (C-RAN): the data-sharing strategy
and the compression-based strategy. Under the former strategy, each user's
message is multicast from the central processor (CP) to all the serving remote
radio heads (RRHs) over the fronthaul network, which then cooperatively serve
the users through joint beamforming; while under the latter strategy, the user
messages are first beamformed then quantized at the CP, and the compressed
signal is unicast to the corresponding RRH, which then decompresses its
received signal for wireless transmission. Previous works show that in general
the compression-based strategy outperforms the data-sharing strategy. This
paper, on the other hand, point s out that in a C-RAN model where the RRHs are
connected to the CP via multi-hop routers, data-sharing can be superior to
compression if the network coding technique is adopted for multicasting user
messages to the cooperating RRHs, and the RRH's beamforming vectors, the
user-RRH association, and the network coding design over the fronthaul network
are jointly optimized based on the techniques of sparse optimization an d
successive convex approximation. This is in comparison to the compression-based
strategy, where information is unicast over the fronthaul network by simple
routing, and the RRH's compression noise covariance and beamforming vectors, as
well as the routing strategy over the fronthaul network are jointly optimized
based on the successive convex approximation technique. The observed gain in
overall network throughput is due to that information multicast is more
efficient than information unicast over the multi-hop fronthaul of a C-RAN.
