It is well-known that, without access to application-layer parameters controlled by Over-The-Top (OTT) providers, Mobile Network Operators (MNOs) struggle to accurately predict customers’ Quality of Experience (QoE). While some previous proposals have suggested interaction between OTTs and MNOs, they have faced challenges in terms of practical implementation and limited application scenarios. This work aims to advance these solutions with two key contributions. First, following the Open Radio Access Network (O-RAN) architecture, we propose adding components that integrate a machine learning (ML)-based QoE prediction model, deployed by the MNO, into the O-RAN system. By establishing specific data-sharing interfaces between OTTs and MNOs, our approach helps MNOs overcome the limitations in updating their quality prediction modules. Second, we present a network-aware, ML-driven QoE prediction model that captures the relationship between the resulting QoE and various network parameters, such as signal-to-interference-noise ratio (SINR), channel quality indicator (CQI), network resource blocks (RBs), throughput, and device mobility. Among seven considered ML regressors, the Gradient Boosting (GB) achieved the highest QoE prediction performance in terms of R 2 (0.906) and RMSE (0.259).

The envisaged fifth-generation (5G) and beyond networks offer unprecedented breakthroughs in media service delivery, stringent requirements, and challenging use cases. In such context, point-to-multipoint communications have proved to be highly efficient in delivering high-quality multimedia services over wireless networks. For the upcoming Beyond-5G (B5G) system releases, the Multicast/Broadcast Services (MBS) capability is an appealing feature that addresses the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. This paper addresses the multicast users’ channel quality diversity throughout multi-rate multicasting aided non-orthogonal multiple access (NOMA) over B5G MBS use cases. We propose a tailored radio resource management (RRM) strategy aided by K-Means subgrouping to deal with such diversity. We characterize and identify conditions for an effective coexistence of the benchmarks conventional multicast scheme (CMS) and subgrouping based on OMA (SOM) with the proposed subgrouping based on NOMA (SNOM). The proposed solution is assessed under a wide range of users’ distributions, throughput requirements, and network conditions of an MBS use case recreated through link-level millimeter-wave (mmWave) simulations.

The increasing demand for quality from multimedia service users is very often addressed by adding more resources (bandwidth and processing power). However, not always does this approach bring an improvement in the perceived quality, whereas it frequently implies an increase in energy consumption (and subsequent higher greenhouse gas emissions). Accordingly, in this paper, we propose a solution to dynamically allocate resources in a 5G heterogeneous network scenario, which aims to identify a trade-off between the overall QoE perceived by the users served by the network when consuming video content and the overall network energy consumption. We considered three types of devices (TV, laptop, and smartphone) for which appropriate QoE and energy consumption models are defined. Extensive simulations have been performed by assigning different levels of importance to QoE and energy. The achieved results show that the network energy consumption can be more than halved by keeping satisfactory QoE. This is particularly true for smartphone users, whereas TV and laptop users have the freedom to choose based on their sensitivity towards sustainability.

The development of future wireless networks focuses on providing services with strict, dynamic, and diverse quality of service (QoS) requirements. In this sense, the network slicing paradigm arises as a critical piece on the efficient allocation and management of network resources, allowing for dividing the network into several logical networks with specific functionalities and performance. This paper aims at finding the best combination of access network and network slices over a heterogeneous environment to fulfill users’ requests and optimize network resources usage. We propose the Dynamic radio Access selection and Slice Allocation (DASA) algorithm, flexibly adapted to network conditions, user priorities, and mobility behavior. DASA is based on a multi-attribute decision making (MADM) and analytical hierarchy process (AHP) to face the complex problem of network selection. Moreover, it uses a cooperative game theory approach to handle load balancing during overload situations. This work presents an integral solution that combines software-defined network (SDN) and network function virtualization (NFV) technologies to improve network performance and user satisfaction. DASA algorithm is evaluated through network-level simulations, focusing on flexibility and the effective utilization of network resources during network selection and load balancing mechanisms.

We investigate the feasibility of Internet of Things (IoT) technology to monitor and improve the energy efficiency and spectrum usage efficiency of broadcasting networks in the Ultra‐High Frequency (UHF) band. Traditional broadcasting networks are designed with a fixed radiated power to guarantee a certain service availability. However, excessive fading margins often lead to inefficient spectrum usage, higher interference, and power consumption. We present an IoT‐based management platform capable of dynamically adjusting the broadcasting network radiated power according to the current propagation conditions. We assess the performance and benchmark two IoT solutions (i.e., LoRa and NB‐IoT). By means of the IoT management platform the broadcasting network with adaptive radiated power reduces the power consumption by 15% to 16.3% and increases the spectrum usage efficiency by 32% to 35% (depending on the IoT platform). The IoT feedback loop power consumption represents less than 2% of the system power consumption. In addition, white space spectrum availability for secondary wireless telecommunications services is increased by 34% during 90% of the time.