  Benefited from the advances of deep learning (DL) techniques, deep joint
source-channel coding (JSCC) has shown its great potential to improve the
performance of wireless transmission. However, most of the existing works focus
on the DL-based transceiver design of the JSCC model, while ignoring the
resource allocation problem in wireless systems. In this paper, we consider a
downlink resource allocation problem, where a base station (BS) jointly
optimizes the compression ratio (CR) and power allocation as well as resource
block (RB) assignment of each user according to the latency and performance
constraints to maximize the number of users that successfully receive their
requested content with desired quality. To solve this problem, we first
decompose it into two subproblems without loss of optimality. The first
subproblem is to minimize the required transmission power for each user under
given RB allocation. We derive the closed-form expression of the optimal
transmit power by searching the maximum feasible compression ratio. The second
one aims at maximizing the number of supported users through optimal user-RB
pairing, which we solve by utilizing bisection search as well as Karmarka' s
algorithm. Simulation results validate the effectiveness of the proposed
resource allocation method in terms of the number of satisfied users with given
resources.
