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Media services must ensure an enhanced user’s perceived quality during content playback to attract and retain audiences, especially while the streams are distributed remotely via networks. Thus, media streaming services rely heavily on good and predictable network performance when delivered to a large number of people. Furthermore, as the quality of media content gets high, the network performance demands are also increasing, and meeting them is challenging. Network functions devoted to improving media streaming services become essential to cope with the high dynamics of network performance and user mobility. Furthermore, new networking paradigms and architectures under the 5G networks umbrella are bringing new possibilities to deploy smart network functions, which monitor the media streaming services through live and objective metrics and boost them in real-time. This survey overviews the state-of-the-art technologies and solutions proposed to apply new network functions for enhancing media streaming.

The latest Heterogeneous Network (HetNet) environments, supported by 5th generation (5G) network solutions, include small cells deployed to increase the traditional macrocell network performance. In HetNet environments, before data transmission starts, there is a user association (UA) process with a specific base station (BS). Additionally, during data transmission, diverse resource allocation (RA) schemes are employed. UA-RA solutions play a critical role in improving network load balancing, spectral performance, and energy efficiency. Although several studies have examined the joint UA-RA problem, there is no optimal strategy to address it with low complexity while also reducing the time overhead. We propose two different versions of simulated annealing (SA): Reduced Search Space SA (RS3A) and Performance-Improved Reduced Search Space SA (P IRS3A), algorithms for solving UA-RA problem in HetNets. First, the UA-RA problem is formulated as a multiple knapsack problem (MKP) with constraints on the maximum BS capacity and transport block size (TBS) index. Second, the proposed RS3A and P IRS3A are used to solve the formulated MKP. Simulation results show that the proposed scheme P IRS3A outperforms RS3A and other existing schemes such as Default Simulated Annealing (DSA), and Default Genetic Algorithm (DGA) in terms of variability and DSA and RS3A in terms of Quality of Service (QoS) metrics, including throughput, packet loss ratio (PLR), delay and jitter. Simulation results show that P IRS3A generates solutions that are very close to the optimal solution.

The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e., cross-tier interference, co-tier interference, and user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions for advanced mechanisms are presented.

Edge computing provides the potential to improve users' Quality of Experience (QoE) in ever-increasing video delivery. However, existing edge-based solutions cannot fully utilize the edge computing power and storage capacity. This paper proposes VIdeo Super-resolution and CAching (VISCA), an edge-assisted adaptive video streaming solution, which integrates super-resolution and edge caching to improve users' QoE. We design a novel edge-based ABR algorithm that makes bitrate and video chunk source decisions by considering network conditions, QoE objectives, and edge resource availability jointly. VISCA utilizes super-resolution to enhance the cached low-quality video at the edge. The super-resolution models used are trained for the most popular videos only in order to achieve quality improvements with a fraction of the computation. A novel cache strategy is also adopted to maximize caching efficiency. To assess the performance of VISCA, an implemented prototype of VISCA was deployed in synthetic and real network contexts. Compared with the existing video streaming solutions, VISCA improves video quality by 28.2%-251.2% and reduces rebuffering time by 16.1%-95.6% in all considered scenarios.

Recently, 360° or omnidirectional videos have become increasingly popular for both personal and enterprize use-cases. However, 360° video streaming has very high bandwidth and processing requirements. State-of-the-art viewport-based streaming solutions lower these requirements by performing selective streaming based on long-term Field-of-View (FoV) prediction mechanisms. However, sometimes user movement is extremely unpredictable during some parts of the video, and applying these solutions adversely affects the overall quality of experience (QoE). This paper proposes a novel Combined Field-of-View tile-based adaptive streaming solution (CFOV) that improves end-user QoE for 360° video streaming. CFOV performs interactive tile selection based on more accurate dynamical viewing area identification by combining the results of two FoV prediction mechanisms. It also employs an innovative priority-based bitrate adaptation approach that ensures improved bitrate budget distribution between different tiles. We evaluate the proposed solution with a comprehensive set of experiments involving four immersive videos, diverse tiling patterns (i.e., 4×3, 6×4, and 8×6), different segment lengths (i.e., 1s, 2s, and 3s), and 48 empirical head movement traces under different bandwidth settings. The evaluation employs a newly defined QoE metric specifically introduced to assess the streaming performance of 360° videos objectively. The experimental findings show that, compared to alternative approaches, our proposed solution can achieve a higher viewport match and can significantly improve the user QoE for different watching behaviors and content characteristics.

In recent years, eXtended Reality (XR) applications have been widely employed in various scenarios, e.g., health care, education, manufacturing, etc. Such application are now easily accessible via mobile phones, tablets, or wearable devices. However, such devices normally suffer from constraints in terms of battery capacity and processing power, limiting the range of applications supported or lowering Quality of Experience. One effective way to address these issues is to offload the computation tasks to the edge servers that are deployed at the network edges, e.g., base stations or WiFi access point, etc. This communication fashion, also named as Multi-access Edge Computing (MEC), is proposed to overcome the limitations in terms of long latency due to long propagation distance of traditional cloud computing approach. XR devices, that are limited in computation resources and energy, can then benefit from offloading the computation intensive tasks to MEC servers. However, as XR applications are comprised of multiple tasks with variety of requirements in terms of latency and energy consumption, it is important to make decision whether one task should be offloaded to MEC server or not. This paper proposes a Deep Reinforcement Learning-based offloading scheme for XR devices (DRLXR). The proposed scheme is used to train and derive the close-to optimal offloading decision whereas optimizing a utility function optimization equation that considers both energy consumption and execution delay at XR devices. The simulation results show how our proposed scheme outperforms the other counterparts in terms of total execution latency and energy consumption.

Base station caching and multicast are two promising methods to support mass content delivery in future wireless network environments. However, existing scheduling designs do not take full advantage of the two methods. This article focuses on employing multicast scheduling and caching in a network architecture which involves both macro cell base stations (MBS) and small cell base stations (SBS) in order to achieve joint optimization of average delay and power consumption. We describe this co-optimization problem as the Multicast-Aware Caching Scheduling Problem (MACSP). This article proposes a novel pending request queue model, which aims to solve the problem of long waiting time for non-popular content, and transform this collaborative multicast-cache scheduling problem into a Markov Decision Process that can be solved using reinforcement learning methods. For actual deployment, the paper further introduces a Distributed Policy Gradient algorithm (DPG) with similar performance and lower complexity. The simulation-based testing results demonstrate that our model and algorithm have better performance and lower energy consumption than existing state-of-the-art approaches.

The latest advances in terms of network technologies open up new opportunities for high-end applications, including using the next generation video streaming technologies. As mobile devices become more affordable and powerful, an increasing range of rich media applications could offer a highly realistic and immersive experience to mobile users. However, this comes at the cost of very stringent Quality of Service (QoS) requirements, putting significant pressure on the underlying networks. In order to accommodate these new rich media applications and overcome their associated challenges, this paper proposes an innovative Machine Learning-based scheduling solution which supports increased quality for live omnidirectional (360°) video streaming. The proposed solution is deployed in a highly dynamic Unmanned Aerial Vehicle (UAV)-based environment to support immersive live omnidirectional video streaming to mobile users. The effectiveness of the proposed method is demonstrated through simulations and compared against three state-of-the-art scheduling solutions, such as: static Prioritization (SP), Required Activity Detection Scheduler (RADS) and Frame Level Scheduler (FLS). The results show that the proposed solution outperforms the other schemes involved in terms of PSNR, throughput and packet loss rate.

Recently, innovative technologies such as Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Multi-Sensorial Media (mulsemedia) have introduced new sensorial effects including vibration, smell, airflow, etc. to human life. These effects which have been largely deployed for entertainment, and gaming have positively impacted user satisfaction. This paper explores the potential of mulsemedia in the education context. It describes a novel Dynamic Adaptive Streaming over HTTP (DASH)-based Multi-sensory Media Delivery Solution (DASHMS) which supports adaptive mulsemedia content distribution based on the operational environment which includes network, device, and user settings.DASHMS was evaluated in a real-life educational experiment involving 44 students in an Irish university. The evaluation focused on both learner satisfaction, and the impact on learning. The results demonstrate the potential of adaptive multi-sensorial media delivery to result in a statistically significant increase in user experience. In terms of benefit to learning outcomes however, it was only memory recall which was statistically improved in the experiment.

Network slicing technology delivers tailored network resources to individual applications on demand. This article first studies the network demands of the most popular UAV applications. These applications are classified according to their quality of service (QoS) requirements. A novel network slicing framework, AirSlice, is designed in the 5G domain to enable differentiated QoS support for UAV applications. Proof-of-concept deployment of AirSlice is presented. AirSlice intends to inspire new businesses, new operations, and new experiences for both operators and UAV service providers.

This paper investigates the possibility of co-channel secondary transmission over the operational digital terrestrial video (DTV) broadcast bands within the interference limits of the DTV receivers. Aided by emulated DTV transmission experiments, secondary transmission-caused interference to the DTV receiver is analyzed. Using this understanding of interference behavior, a new transmission scheme for the secondary network nodes is proposed. The scheme selects the best communication parameters in terms of transmit power, spectral overlap, temporal occupancy, symbol duration, and modulation scheme to achieve a desired quality of service target within the interference limits of the DTV receivers. This is achieved without having information about the presence of DTV receivers. The feasibility as well as limits of the proposed secondary deployment scheme are then analyzed. This study is expected to serve as a valuable planning tool in deploying cognitive secondary networks over DTV transmission bands.

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART , a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions.

The integration of a broadcast oriented RAN architecture into a convergent framework for heterogeneous networks offers two major contributions. On the one hand, it presents an interesting solution to satisfy the exponential growth of the mobile video data traffic, and on the other hand, it is also identified as a promising key technology, that can contribute to improve the energy efficiency of 5G networks. This work proposes a novel Convergent Architecture for Broadcast, Broadband and Cellular services (CABS). This architecture is complemented with the Performance and Energy-aware Access (PEC) network selection algorithm. This joint solution offers a balanced trade-off between the user perceived QoS and the network energy efficiency in challenging heterogeneous scenarios. Furthermore, this approach guarantees the broadcast service continuity in harsh environments, and therefore, is helpful in one of the most critical scenarios for broadcasting industry: mobile and indoor reception. The proposed solution was modelled and tested in Network Simulator version 3 (NS-3), in which among other updates, a first version of an ATSC 3.0 model was introduced.

Quality of Experience (QoE) reflects end users’ overall experience and feeling with network services, but needs support in terms of end-to-end Quality of Service (QoS). Segment routing (SR) as a new routing paradigm can provide good end-to-end QoS guarantee, making traditional multimedia traffic routing more efficient and scalable. In this paper, we address two problems related to the new SR mechanism: enabling fine-grained end-to-end QoS routing under a complex network environment and constructing the multicast routing tree with branch node load balancing. To solve these problems, an Inaccurate information-based QoE-driven Routing algorithm (IQdR) and a Branch-aware Multicast Tree (BaMT) algorithm were proposed. Simulation test results that have compared the performance of our proposed solution against that of other algorithms show that the previous works were outperformed. Additionally, the results also show that our multicast architecture improves the scalability of the network in terms of the number of flows.

There is an important trend in terms of user expectations of ubiquity in relation to rich media services and increase in number and range of devices with high specifications which enable access to such services, with very ambitious technical requirements. Providing this support is a very challenging issue, especially in urban dense network environments (DenseNet). Diverse solutions have been proposed, including deployment of femtocells in conjunction with the existing infrastructure, but there is still need for new approaches to balance resources and quality in current competitive market. This paper proposes an innovative three-phase adaptive scheduling solution [energy-quality utility-based adaptive scheduling solution (EQUAS)], which performs trade off between service quality and energy efficiency when allocating network resources to mobile users in a DenseNet. Resource allocation is performed according to a utility function that takes into account throughput, device energy consumption, and user mobility. Furthermore, adaptive reallocation is applied to increase network coverage and avoid dropping service. Testing results show how EQUAS outperforms two competitive approaches in terms of energy consumption and efficiency, data throughput, and estimated user satisfaction.

Contribution: The NEWTON fabrication laboratory (Fab Lab) Education Initiative assesses the effectiveness of Fab Lab-based learning on K–12 students’ attitude toward learning science, technology, engineering, art, and mathematics (STEAM). It considers two aspects: 1) students’ motivation and affective state and 2) students’ observations and perception of Fab Lab-based learning. Background: Fab Labs are described as small workshops equipped with a set of computer-controlled tools (e.g., 3-D printers) that offer personalized digital fabrication. They have been shown to have a positive impact on learners’ academic and personal growth when used in extracurricular activities, but little research examines whether integrating Fab Labs into school curricula can help raise student interest in STEAM education. Research Question: Can Fab Lab-based learning foster students’ interest in STEM in primary and secondary schools? Would students be keen to use Fab Labs in their science classes? Methodology: Two case studies were carried out in two different schools as part of the European Horizon 2020 NEWTON project. The study had 39 participants; three different surveys were used to assess different constructs. Findings: Results show that after using the Fab Lab-based learning: 1) students felt more interested in science classes as well as more engaged and less bored and 2) while some students struggled at first when manipulating Fab Lab tools, they adapted quickly and reported that they would like to use Fab Labs as part of their science classes.

Omnidirectional or 360° video is increasingly being used, mostly due to the latest advancements in immersive Virtual Reality (VR) technology. However, its wide adoption is hindered by the higher bandwidth and lower latency requirements than associated with traditional video content delivery. Diverse researchers propose and design solutions that help support an immersive visual experience of 360° video, primarily when delivered over a dynamic network environment. This paper presents the state-of-the-art on adaptive 360° video delivery solutions considering end-to-end video streaming in general and then specifically of 360° video delivery. Current and emerging solutions for adaptive 360° video streaming, including viewport-independent, viewport-dependent, and tile-based schemes are presented. Next, solutions for network-assisted unicast and multicast streaming of 360° video content are discussed. Different research challenges for both on-demand and live 360° video streaming are also analyzed. Several proposed standards and technologies and top international research projects are then presented. We demonstrate the ongoing standardization efforts for 360° media services that ensure interoperability and immersive media deployment on a massive scale. Finally, the paper concludes with a discussion about future research opportunities enabled by 360° video.

The Multi-Access Edge Computing (MEC) paradigm provides a promising solution for efficient computing services at edge nodes, such as base stations (BS), access points (AP), etc. By offloading highly intensive computational tasks to MEC servers, critical benefits in terms of reducing energy consumption at mobile devices and lowering processing latency can be achieved to support high Quality of Service (QoS) to many applications. Among the services which would benefit from MEC deployments are eXtended Reality (XR) applications which are receiving increasing attention from both academia and industry. XR applications have high resource requirements, mostly in terms of network bandwidth, computation and storage. Often these resources are not available in classic network architectures and especially not when XR applications are run by mobile devices. This paper leverages the concepts of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to propose an innovative resource management scheme considering heterogeneous QoS requirements at the MEC server level. The resource assignment is formulated by employing a Deep Reinforcement Learning (DRL) technique to support high quality of XR services. The simulation results show how our proposed solution outperforms other state-of-the-art resource management-based schemes.

There is an increasing user demand for high-quality content-rich multimedia services. Despite their advantages, current wireless networks in general and wireless mesh networks in particular have limitations in terms of quality of service (QoS) provisioning, especially when dealing with increased amounts of time sensitive traffic such as video. This paper presents video load balancing solution (ViLBaS), a load balancing-based mechanism which enhances delivery performance of video services over multi-hop wireless mesh networks and improves user quality of experience (QoE) levels. ViLBaS involves performance monitoring at the level of the nodes and load balancing by off-loading traffic from loaded nodes to less loaded neighboring nodes. Simulation-based results show how the proposed ViLBaS improves video delivery performance in terms of both QoS and QoE metrics [delay, throughput, packet loss, and peak signal-to-noise-ratio (PSNR)]. The comparison is performed against three other traditional approaches in different network topologies, for diverse video flow distributions, and different sizes for the video queue.

The Multi-access Edge Computing (MEC) paradigm offers cloud-computing support to rich media applications, including Dynamic Adaptive Streaming over HTTP (DASH)-based ones at the edge of the network, close to mobile users. MEC servers, typically deployed at base stations (BS), help reduce latency and improve quality of experience (QoE) of video streaming. Unfortunately the communications involving mobile users require handovers between BSs and these influence both transmission efficiency because of the relative position of the MEC servers and transit cost. At the same time, serving MEC for a mobile user should not necessarily be changed when handover occurs. This paper introduces QoE Ready to Respond (QoE-R2R), a QoE-aware MEC Selection scheme for DASH-based mobile adaptive video streaming for optimizing video transmission in a MEC-supported network environment. Simulation-based testing shows that the proposed (QoE-R2R) scheme outperforms some traditional alternative solutions. Compared to hit rate and delay-based schemes, QoE-R2R reduces by 27.6% transmission time and improves with 6.2% QoE.

Resource management in emerging dense heterogeneous network environments (DenseNets) is a challenging issue. The employment of multicast transmissions in this scenario has potential to address the problems. On one hand, the large number of smart user mobile devices and user expectations for high-quality rich media services has determined a growing demand for network resources; in DenseNets, mobile users have to make the choice in terms of the network to connect to, in order to balance energy saving and delivery performance. On the other hand, the proliferation of user accesses to the existing and future network infrastructure will bring along with it the operators need for optimizing the radio resource usage. This paper proposes a hybrid unicast-multicast utility-based network selection algorithm (HUMANS), which offers the additional option of selecting multicast transmissions in the network selection process during video delivery. By serving users with good channel conditions via unicast transmissions and users with poor channel quality conditions via multicast, HUMANS allows outperforming other solutions in terms of outage percentage and average quality of transmission, in both low- and high-density scenarios. Most importantly, at the same time it guarantees operators a more efficient resource utilization.

Virtual reality (VR) is currently being used in many different areas such as car prototyping, gaming, medical training, teaching, and so on. The Internet of Things (IoT) devices such as systems-on-a-chip (e.g. Raspberry Pi), smart appliances, and sensors support a wide range of services, including machine automation, and remote monitoring and control. This paper introduces a novel social VR-IoT environment, which allows users to share and control local or remote IoT devices in a virtual platform. Two approaches using the VR-IoT solution are presented: one local network-based and one cloud-based. The proposed VR-IoT environment contains VRITESS, the novel VR-IoT Environment Synchronization Scheme, which facilitates a consistent and integrated experience for users by enabling control of real IoT objects with VR headsets. The control of some IoT objects in extreme environments or devices which are complex to operate can be simplified in a virtual environment. The VRITESS synchronization scheme maintains the real objects updated following instructions given in the virtual world and vice-versa. Testing involved local network-based and cloud-based testbeds created with a VR headset and IoT devices with the Performance Engineering Laboratory, Dublin City University's, Ireland. Test results demonstrated that lower latency is experienced in the local-network testbed in comparison with the cloud testbed. Furthermore, tests regarding the communications protocols implemented in the cloud testbed indicated that MQTT generates less delay and data traffic than REST.

Increasing number of researchers are focusing on the emerging communication technologies which enrich user perceived quality of experience by involving vision, auditory, tactile, olfaction, gustatory, and other senses. However, there are multiple challenges related to using multiple sensorial media (i.e., mulsemedia), including synchronization with the traditional multimedia content and delivery over diverse network environments. This paper proposes MulseDASH, a novel multiple sensorial media content delivery solutions based on the Dynamic Adaptive Streaming over HTTP standard (DASH). MulseDASH is described and evaluated in a real test-bed in terms of the effectiveness of its adaptive streaming and synchronization mechanisms. The extensive testing involving both network emulation and subjective assessment experiments shows how MulseDASH performs an excellent real-time streaming adjustment to match network conditions and improves user quality of experience.

UAVs are powerful IoT components, offering sensing, communications and data analysis in the air. Recently, 3GPP TS 22.261, TR 22.862, and TR 36.777 have specified performance requirements for communications between multiple UAVs in the 5G domain. This article discusses communications reliability challenges in a UAV swarm context. Recommendations for designing an ultra-reliable communications system for UAV swarms are introduced with a focus on the software protocol stack and RF hardware. For the purpose of demonstration, we developed Easy- Swarm, an open-source UAV swarming platform that adopts the LoRa radio at the physical layer and a low-latency channel access protocol at the MAC layer. Real-life testbeds are built consisting of 10 UAVs and 20 robotic cars that produce background traffic. LoRa, WiFi and LTE networks are employed to provide broadband and cellular wireless network support. Results show that using LoRa leads to better reliability, in particular allowing for higher swarm density and longer coverage distance, than when WiFi is used. In addition, LTE provides the best reliability and latency for UAV swarms with good network connectivity.

In a fog computing (FC) architecture, cloud services migrate towards the network edge and operate via edge devices such as access points (AP), routers, and switches. These devices become part of a virtualization infrastructure and are referred to as “fog nodes”. Recently, software-defined networking (SDN) has been used in FC to improve its control and manageability. The current SDN-based FC literature has overlooked two issues: (a) fog nodes’ deployment at optimal locations and (b) SDN best path computation for data flows based on constraints (i.e., end-to-end delay and link utilization). To solve these optimization problems, this paper suggests a novel approach, called scalable and optimal near-sighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems (SOSW). First, the SOSW model uses singular-value decomposition (SVD) and QR factorization with column pivoting linear algebra methods on the traffic matrix of the network to compute the optimal locations for fog nodes, and second, it introduces a new heuristic-based traffic engineering algorithm, called the constraint-based shortest path algorithm (CSPA), which uses ant colony optimization (ACO) to optimize the path computation process for task offloading. The results show that our proposed approach significantly reduces average latency and energy consumption in comparison with existing approaches.

The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pressure on the underlying network infrastructure. This affects negatively content delivery and consequently user perceived quality, especially for video-based services. Focusing on this problem, this paper proposes a machine learning-based resource allocation solution that improves the quality of video services for increased number of viewers. The solution is deployed and tested in an educational context, demonstrating its benefit in terms of major quality of service parameters for various video content, in comparison with existing state of the art. Moreover, a discussion on how the technology is helping to mitigate the effects of massively increasing Internet traffic on the video quality in an educational context is also presented.