  We consider the problem of rate/distortion with side information available
only at the decoder. For the case of jointly-Gaussian source X and side
information Y, and mean-squared error distortion, Wyner proved in 1976 that the
rate/distortion function for this problem is identical to the conditional
rate/distortion function R_{X|Y}, assuming the side information Y is available
at the encoder. In this paper we construct a structured class of asymptotically
optimal quantizers for this problem: under the assumption of high correlation
between source X and side information Y, we show there exist quantizers within
our class whose performance comes arbitrarily close to Wyner's bound. As an
application illustrating the relevance of the high-correlation asymptotics, we
also explore the use of these quantizers in the context of a problem of data
compression for sensor networks, in a setup involving a large number of devices
collecting highly correlated measurements within a confined area. An important
feature of our formulation is that, although the per-node throughput of the
network tends to zero as network size increases, so does the amount of
information generated by each transmitter. This is a situation likely to be
encountered often in practice, which allows us to cast under new--and more
``optimistic''--light some negative results on the transport capacity of
large-scale wireless networks.
