  The cloud radio access network (C-RAN) has become the foundational structure
for various emerging communication paradigms, leveraging the flexible
deployment of distributed access points (APs) and centralized task processing.
In this paper, we propose a cross-layer optimization framework based on a
practical finite-length coding communication system in C-RAN, aiming at
maximizing bandwidth efficiency while providing statistical quality of service
(QoS) for individual services. Based on the theoretical results from effective
capacity and finite-length coding, we formulate a joint optimization problem
involving modulation and coding schemes (MCS), retransmission count, initial
bandwidth allocation and AP selection, which reflects the coordinated decision
of parameters across the physical layer, data link layer and transport layer.
To tackle such a mixed-integer nonlinear programming (MINLP) problem, we
firstly decompose it into a transmission parameter decision (TPD) sub-problem
and a user association (UA) sub-problem, which can be solved by a binary
search-based algorithm and an auction-based algorithm respectively. Simulation
results demonstrate that the proposed model can accurately capture the impact
of QoS requirements and channel quality on the optimal transmission parameters.
Furthermore, compared with fixed transmission parameter setting, the proposed
algorithms achieve the bandwidth efficiency gain up to 27.87% under various
traffic and channel scenarios.
