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

Cross-Layer Optimization for Statistical QoS Provision in C-RAN with Finite-Length Coding