More refined resource management and Quality of Service (QoS) provisioning is a critical goal of wireless communication technologies. In this paper, we propose a novel Business-Centric Network (BCN) aimed at enabling scalable QoS provisioning, based on a cross-layer framework that captures the relationship between application, transport parameters, and channels. We investigate both continuous flow and event-driven flow models, presenting key QoS metrics such as throughput, delay, and reliability. By jointly considering power and bandwidth allocation, transmission parameters, and AP network topology across layers, we optimize weighted resource efficiency with statistical QoS provisioning. To address the coupling among parameters, we propose a novel deep reinforcement learning (DRL) framework, which is Collaborative Optimization among Heterogeneous Actors with Experience Sharing (COHA-ES). Power and sub-channel (SC) Actors representing multiple APs are jointly optimized under the unified guidance of a common critic. Additionally, we introduce a novel multithreaded experience-sharing mechanism to accelerate training and enhance rewards. Extensive comparative experiments validate the effectiveness of our DRL framework in terms of convergence and efficiency. Moreover, comparative analyses demonstrate the comprehensive advantages of the BCN structure in enhancing both spectral and energy efficiency.

As a ubiquitous deployment paradigm, integrating microservice architecture (MSA) into edge networks promises to enhance the flexibility and scalability of services. However, it also presents significant challenges stemming from dispersed node locations and intricate network topologies. In this paper, we have proposed a topology-aware MSA characterized by a three-tier network traffic model encompassing the service, microservices, and edge node layers. This model meticulously characterizes the complex dependencies between edge network topologies and microservices, mapping microservice deployment onto link traffic to accurately estimate communication delay. Building upon this model, we have formulated a weighted sum communication delay optimization problem considering different types of services. Then, a novel topology-aware and individual-adaptive microservices deployment (TAIA-MD) scheme is proposed to solve the problem efficiently, which accurately senses the network topology and incorporates an individual-adaptive mechanism in a genetic algorithm to accelerate the convergence and avoid local optima. Extensive simulations show that, compared to the existing deployment schemes, TAIA-MD improves the communication delay performance by approximately 30% to 60% and effectively enhances the overall network performance. Furthermore, we implement the TAIA-MD scheme on a practical microservice physical platform. The experimental results demonstrate that TAIA-MD achieves superior robustness in withstanding link failures and network fluctuations.

Millimeter-wave$\!$(mmWave) technology has emerged as a pivotal catalyst for unleashing the full potential of 360° virtual reality (VR). Nonetheless, the explosive growth of VR services, combined with the quality-of-service (QoS) provisioning issues of mmWave, poses formidable challenges in wireless resource allocation for mmWave-enabled 360° VR. In this paper, we propose an innovative 360° VR streaming architecture that addresses three underexplored issues: overlapping fields-of-view (FoVs), statistical QoS provisioning (SQP), and loss-tolerant active data discarding. Specifically, we first design an overlapping FoV-based optimal joint unicast and multicast (JUM) task assignment scheme, which significantly conserves wireless resources by implementing non-redundant task allocation. Leveraging stochastic network calculus (SNC), we develop a comprehensive SNC-based SQP theoretical framework from delay and rate perspectives. Additionally, we propose two corresponding optimal adaptive joint resource allocation and active-discarding (ADAPT-JRAAD) transmission schemes to minimize resource consumption while guaranteeing SQP performance from delay and rate perspectives, respectively. Extensive simulations demonstrate the outstanding performance of the designed optimal JUM task assignment scheme in conserving wireless resources. Moreover, comprehensive comparisons against six benchmarks validate the superiority of the proposed two ADAPT-JRAAD schemes in resource utilization, flexible rate control, and robust queue management.

Industry 5.0 and beyond networks have driven the emergence of numerous mission-critical services exemplified by neXt-generation ultra-reliable low-latency communication (xURLLC). To guarantee low-latency requirements, xURLLC heavily relies on short-blocklength packets with sporadic arrival traffic. As a disruptive multi-access technique, rate-splitting multiple access (RSMA) has emerged as a promising avenue to enhance the quality of service (QoS) and flexibly manage interference for xURLLC. In this paper, we study an innovative RSMA-assisted uplink xURLLC industrial internet-of-things (IIoT) (RSMA-xURLLC-IIoT) network, which takes into account imperfect CSI and finite blocklength (FBL) regimes. We develop a novel theoretical framework leveraging stochastic network calculus (SNC) aimed at revealing insights into statistical QoS provisioning (SQP). Building upon this framework, we formulate the SQP-driven short-packet size maximization and transmit power minimization problems, aiming to guarantee the SQP performance to delay, decoding, and reliability while maximizing the short-packet size and minimizing the transmit power, respectively. By exploiting Monte-Carlo methods, we have thoroughly validated the reliability of the developed theoretical framework. Moreover, through extensive comparison analysis with state-of-the-art multi-access techniques, including non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA), we have demonstrated the superior performance gains achieved by the proposed network.

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.

In view of the stringent requirements for delay in 5G usage scenarios, the existing congestion control schemes based on terminal measurement and evaluation cannot meet the requirements due to their reactive nature and blindness to links. To this end, we propose a buffer state-driven rate control mechanism in 5G networks, namely active queue management (AQM) based buffer delay guarantee (AQM-BDG) to explicitly adjust the sending rate of the source. Specifically, the rate mismatch and buffer state in Radio Link Control (RLC) layer are comprehensively considered, which timely grasp the dynamics of the buffer to obtain the direction and urgency of rate adjustment. Then the incoming packets are marked proportionally as "increase" or "decrease" to adjust the sending rate. Meanwhile, the adjustment quantum is reallocated among the different Service Data Flows (SDFs) that are mapped to the same Data Radio Bearer (DRB), so that every SDF can obtain the throughput share according to the preset priority, no matter what rate it starts at. Experimental results demonstrate that AQM-BDG significantly outperforms classical congestion control schemes on network utility by achieving relatively high throughput while bounding queuing delay within the threshold.