In this paper, a video service enhancement strategy is investigated under an edge-cloud collaboration framework, where video caching and delivery decisions are made at the cloud and edge respectively. We aim to guarantee the user fairness in terms of video coding rate under statistical delay constraint and edge caching capacity constraint. A hybrid human-artificial intelligence approach is developed to improve the user hit rate for video caching. Specifically, individual user interest is first characterized by merging factorization machine (FM) model and multi-layer perceptron (MLP) model, where both low-order and high-order features can be well learned simultaneously. Thereafter, a social aware similarity model is constructed to transfer individual user interest to group interest, based on which, videos can be selected to cache at the network edge. Furthermore, a dual bisection exploration scheme is proposed to optimize wireless resource allocation and video coding rate. The effectiveness of the proposed video caching and delivery scheme is finally validated by extensive experiments with a real-world dataset.

An efficient resource management strategy can save network resources and improve network quality of service (QoS). In order to solve the problem of resource management difficulties of multiple SPs sharing the same backhaul network and QoS mismatch in heterogeneous networks, a downstream resource management in 5G virtualized backhaul network is proposed in this paper. Firstly, the wavelength assignment problem is described as a mathematics model with the consideration of minimizing turning overhead and load balancing. The quantum-behaved particle swarm optimization algorithm is used to solve the mathematics model. Secondly, the 5G traffic is mapping to different priority queues in TWDM-PON by the proposed QoS mapping algorithm. Finally, the downstream traffic is scheduled by the proposed resource scheduling algorithm. Numerical results show that the proposed mechanism can maintain high resource utilization, reduce wavelength tuning overhead and improve the delay of traffic.

With the tremendous growth of traffic demand caused by the conventional service and emerging Internet-of-Things (IoT) applications, it is vitally challenging to support seamlessly access for billions of IoT devices along with existing Internet service. Fiber-Wireless (FiWi) access networks is an ideal solution for the next-generation access networks to support both IoT and conventional service. One of the main challenges faced by the design of an FiWi access network is the high energy consumption due to low utilization of optical network units (ONUs) and high control overhead of data transmission. In this article, An adaptive frame aggregation scheme with load transfer is proposed to reduce the energy consumption in FiWi. By evaluating the quality of wireless channel, the proposed scheme adaptively adjusts the length of the aggregated frame to reduce the energy consumption caused by the frequent preemption of wireless channel and the numerous retransmission of data frames resulted by poor channel quality. In the optical backhaul, according to the delay requirements of services with different priorities, the proposed scheme calculates the optimal frame length for each queue and performs load transfer among ONUs to dynamically distribute network load and maximize the sleep rate of ONUs for energy saving. Simulation results and theoretical analysis both unanimously show that the proposed scheme achieves significant amounts of energy saving, while preserving delay performance of various services.

By collecting and processing smart healthcare data in real-time, mobile edge networks can bridge the gap between doctors and patients, which is a representative consumer-level data interaction scenario in Industry 5.0. To improve the flexibility and timeliness of suchlike Industry 5.0 services, microservices are deployed extensively on edge servers. The major challenge facing microservice deployment is how to reduce the cost. However, cost and quality of service (QoS) are a pair of contradictory objectives, and different scenarios have different preferences for the two objectives, which is easy to be ignored. In order to solve this problem, this paper considers service responsiveness and user mobility in the response process of requests. Then a Constrained Multi-objective Optimization Problem (CMOP) is formulated by combining deployment cost and task completion rate. Considering the differentiated requirements of users in different scenarios, this paper proposes a Multi-objective Optimization Evolutionary Algorithm with the Penalty Mechanism (MOEA-PM) to solve this CMOP. Simulation results demonstrate that adding a penalty segment to the fitness function can boost algorithm efficiency significantly. Compared with baseline algorithms, MOEA-PM always yields a microservice deployment strategy set adapted to a variety of industry 5.0 scenarios.

With the tremendous growth of the traffic demand of emerging 5G applications, the survivability has become more significant in the wireless-optical broadband access network (WOBAN), since any failure of the network components could interrupt a large amount of traffic. A large amount of data could be lost even if there is a very short interruption. Previous works focus on finding the alternate paths or deploying backup fiber to recover the interrupted traffic. However, these works encounter three potential problems. First, the deployment of a backup fiber could increase the cost of recovery. Second, they must reroute the interrupted traffic to the backup resources, which could cause a long recovery time. Third, a large number of network packets could be lost during the recovery processes. To address these problems, a lossless fault-tolerance mechanism combining with parallel routing and network coding (LFTM-PR-NC) is proposed in this paper to enhance the survivability of WOBAN against any optical network unit-level and wireless-level failures. Numerical results show that the proposed LFTM-PR-NC mechanism significantly outperforms the previous mechanism. Meanwhile, the LFTM-PR-NC mechanism could dramatically decrease the recovery time when a failure occurs.

In this paper, the video delivery strategy is studied under the cooperative framework of cache-enabled fog radio access networks (F-RAN), where multicast beamforming and video-quality selection are both taken into account in terms of different scenarios. In the user-centered scenario, an optimization problem regarding to the network quality of experience (QoE) improvement is first formulated under the essential quality of service (QoS) requirements of the users. Furthermore, an energy efficiency maximization scheme is proposed in the user-dense scenario to improve the resource utilization when network resources may be insufficient to guarantee the QoS requirements of all the users. In particular, a user scheduling scheme is studied in this case. More specifically, the non-convex constraints and fractional object functions in the aforementioned problems are dealt with by successive convex approximation (SCA) technique and the Dinkelbach approach. In order to solve the $l_{0}$ -norm problem caused by user scheduling, we introduce the $l_{1}$ -norm approximation to improve the sparsity of the solution. Finally, the effectiveness of the proposed schemes is validated by extensive simulations.