  Accurate location information is indispensable for the emerging applications
of \ac{iov}, such as automatic driving and formation control. In the real
scenario, vision-based localization has demonstrated superior performance to
other localization methods for its stability and flexibility. In this paper, a
scheme of cooperative vision-based localization with communication constraints
is proposed. Vehicles collect images of the environment and distance
measurements between each other. Then vehicles transmit the coordinates of
feature points and distances with constrained bits to the edge to estimate
their positions. The \ac{fim} for absolute localization is first obtained,
based on which we derive the relative \ac{speb} through subspace projection.
Furthermore, we formulate the corresponding bit allocation problem for relative
localization. Finally, a \ac{vgd} algorithm is developed by considering the
influence of photographing, distance measurements and quantization noises.
Compared with conventional bit allocation methods, numerical results
demonstrate the localization performance gain of our proposed algorithm with
higher computational efficiency.
