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The quality-of-service (QoS)/quality-of-experience (QoE) demands of mobile services are soaring and have overwhelmed the obsolescent capability of 3G and 4G cellular networks. The emerging 5G networks will bring an unprecedented promotion in transmission data rates. However, the satisfaction of some service requirements is still in dilemma, especially the end-to-end (E2E) latency which varies in different applications. Multi-access edge computing (MEC), a promising technology in 5G cellular networks, can provide ultra-low E2E latency and reduce traffic load on mobile backhaul networks. The potential benefits of MEC for 5G and beyond services have been explored by preliminary studies. What remains is the uncertainty of revenue from the investment of MEC which will shake operators’ decisions about whether and how to deploy MEC in cellular networks. In this light, this paper designs a MEC-assisted 5G and beyond ecosystem inclusive of three players: private (local) telecom operators, backhaul, and cloud service owners. We propose a revenue maximization model for private (local) telecom operators and cloud service owners to minimize the cost from the end-user perspective while satisfying the latency requirement. The derived model indicates that two players’ revenues can be maximized by optimizing MEC resources and backhaul capacity. The game-theoretic analyses also reveal the optimized hybrid strategy of MEC and cloud for efficient mobile traffic management.

Recently, the mobile traffic demand is increasing due to the drastic growth in the number of connected devices and smart services utilizing wireless cellular networks. In order to deal with this tough challenge, a key technology in 5G cellular networks, Multi-Access Edge Computing (MEC) which can provide ultra-low end-to-end latency, significant reduction of traffic load on backhaul, etc., has come into sight. The potential benefits of MEC in 5G and beyond have been revealed by plenty of state-of-the-art works. However, it leaves a difficult problem for the operators, which is to determine whether and how to deploy MEC in cellular networks due to the uncertainty of feedback from the investment on MEC. Although, there were a few works studying on the optimization of the number of MEC from the viewpoint of telecom operators, their contribution focus only on telecom operators rather than taking other operators (backhaul owners, Cloud owners, etc.) into account. In this paper, we propose a social maximization revenue model with the investment strategy for the telecom operators who decide the number of MEC and the backhaul owner who lease the backhaul capacity. The numerical results indicate that the optimal number of MEC and backhaul capacity that maximize the two players' profit is the same. Hence, it turns out that the advantage of MEC can be highlighted by using both Cloud and edge resource in parallel rather than processing all traffic on the MEC side.

To deal with recent increasing mobile traffic, ultra-broadband communication with millimeter-wave (mmWave) has been regarded as a key technology for 5G cellular networks. In a previous study, a mmWave heterogeneous network was composed of several mmWave small cells overlaid on the coverage of a macro cell. However, as seen from the optical fiber penetration rate worldwide, it is difficult to say that backhaul with Gbps order is available everywhere. In the case of using mmWave access under a limited backhaul capacity, it becomes a bottleneck at the backhaul; thus, mmWave access cannot fully demonstrate its potential. On the other hand, the concept of multi-access edge computing (MEC) has been proposed to decrease the response latency compared to cloud computing by deploying storage and computation resources to the user side of mobile networks. This paper introduces MEC into mmWave heterogeneous networks and proposes a content prefetching algorithm to resolve such backhaul issues. Context information, such as the destination, mobility, and traffic tendency, is shared through the macro cell to the prefetch application and data that the users request. Prefetched data is stored in the MEC and then transmitted via mmWave without a backhaul bottleneck. The effectiveness is verified through computer simulations where we implement realistic user mobility as well as traffic and backhauling models. The results show that the proposed framework achieved 95% system capacity even under the constraint of a 1 Gbps backhaul link.