Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

MBMS—IP Multicast/Broadcast in 3G Networks

In this article, the Multimedia Broadcast and Multicast Service (MBMS) as standardized in 3GPP is presented. With MBMS, multicast and broadcast capabilities are introduced into cellular networks. After an introduction into MBMS technology, MBMS radio bearer realizations are presented. Different MBMS bearer services like broadcast mode, enhanced broadcast mode and multicast mode are discussed. Streaming and download services over MBMS are presented and supported media codecs are listed. Service layer components as defined in Open Mobile Alliance (OMA) are introduced. For a Mobile TV use case capacity improvements achieved by MBMS are shown. Finally, evolution of MBMS as part of 3GPP standardization is presented

Radio resource allocation algorithms for multicast OFDM systems

Mención Internacional en el título de doctorVideo services have become highly demanded in mobile networks leading to an unprecedented traffic growth. It is expected that traffic from wireless and mobile devices will account for nearly 70 percent of total IP traffic by the year 2020, and the video services will account for nearly 75 percent of mobile data traffic by 2022. Multicast transmission is one of the key enablers towards a more spectral and energy efficient distribution of multimedia content in current and envisaged mobile networks. It is worth noting that multicast is a mechanism that efficiently delivers the same content to many users, not only focusing on video broadcasting, but also distributing many other media, such as software updates, weather forecast or breaking news. Although multicast services are available in Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, new improvements are needed in some areas to handle the demands expected in the near future. Resource allocation techniques for multicast services are one of the main challenging issues, since it is required the development of novel schemes to meet the demands of their evolution towards the next generation. Most multicast techniques adopt rather conservative strategies that select a very robust modulation and coding scheme (MCS), whose characteristics are determined by the propagation conditions experienced by the worst user in the group in order to ensure that all users in a multicast group are able to correctly decode the received data. Obviously, this robustness comes at the prize of a low spectral efficiency. This thesis presents an exhaustive study of broadcast/multicast technology for current mobile networks, especially focusing on the scheduling and resource allocation (SRA) strategies to maximize the potential benefits that multicast transmissions imply on the spectral efficiency. Based on that issue, some contributions have been made to the state of the art in the radio resource management (RRM) for current and beyond mobile multicast services. • In the frame of LTE/LTE-A, the evolved multimedia broadcast and multicast service (eMBMS) shares the physical layer resources with the unicast transmission mode (at least up to Release 12). Consequently, the time allocation to multicast transmission is limited to a maximum of a 60 percent, and the remaining subframes (at least 40 percent) are reserved for unicast transmissions. With the aim of achieving the maximum aggregated data rate (ADR) among the multicast users, we have implemented several innovative SRA schemes that combine the allocation of multicast and unicast resources in the LTE/LTE-A frame, guaranteeing the prescribed quality of service (QoS) requirements for every user. • In the specific context of wideband communication systems, the selection of the multicast MCS has often relied on the use of wideband channel quality indicators (CQIs), providing rather imprecise information regarding the potential capacity of the multicast channel. Only recently has the per-subband CQI been used to improve the spectral efficiency of the system without compromising the link robustness. We have proposed novel subband CQI-based multicast SRA strategies that, relying on the selection of more spectrally efficient transmission modes, lead to increased data rates while still being able to fulfill prescribed QoS metrics. • Mobile broadcast/multicast video services require effective and low complexity SRA strategies. We have proposed an SRA strategy based on multicast subgrouping and the scalable video coding (SVC) technique for multicast video delivery. This scheme focuses on reducing the search space of solutions and optimizes the ADR. The results in terms of ADR, spectral efficiency, and fairness among multicast users, along with the low complexity of the algorithm, show that this new scheme is adequate for real systems. These contributions are intended to serve as a reference that motivate ongoing and future investigation in the challenging field of RRM for broadcast/ multicast services in next generation mobile networks.La demanda de servicios de vídeo en las redes móviles ha sufrido un incremento exponencial en los últimos años, lo que a su vez ha desembocado en un aumento sin precedentes del tráfico de datos. Se espera que antes del año 2020, el trafico debido a dispositivos móviles alcance cerca del 70 por ciento del tráfico IP total, mientras que se prevé que los servicios de vídeo sean prácticamente el 75 por ciento del tráfico de datos en las redes móviles hacia el 2022. Las transmisiones multicast son una de las tecnologías clave para conseguir una distribución más eficiente, tanto espectral como energéticamente, del contenido multimedia en las redes móviles actuales y futuras. Merece la pena reseñar que el multicast es un mecanismo de entrega del mismo contenido a muchos usuarios, que no se enfoca exclusivamente en la distribución de vídeo, sino que también permite la distribución de otros muchos contenidos, como actualizaciones software, información meteorológica o noticias de última hora. A pesar de que los servicios multicast ya se encuentran disponibles en las redes Long Term Evolution (LTE) y LTE-Advanced (LTE-A), la mejora en algunos ámbitos resulta necesaria para manejar las demandas que se prevén a corto plazo. Las técnicas de asignación de recursos para los servicios multicast suponen uno de los mayores desafíos, ya que es necesario el desarrollo de nuevos esquemas que nos permitan acometer las exigencias que supone su evolución hacia la próxima generación. La mayor parte de las técnicas multicast adoptan estrategias conservadoras, seleccionando esquemas de modulación y codificación (MCS) impuestos por las condiciones de propagación que experimenta el usuario del grupo con peor canal, para así asegurar que todos los usuarios pertenecientes al grupo multicast sean capaces de decodificar correctamente los datos recibidos. Como resulta obvio, la utilización de esquemas tan robustos conlleva el precio de sufrir una baja eficiencia espectral. Esta tesis presenta un exhaustivo estudio de la tecnología broadcast/ multicast para las redes móviles actuales, que se centra especialmente en las estrategias de asignación de recursos (SRA), cuyo objetivo es maximizar los beneficios que la utilización de transmisiones multicast potencialmente implica en términos de eficiencia espectral. A partir de dicho estudio, hemos realizado varias contribuciones al estado del arte en el ámbito de la gestión de recursos radio (RRM) para los servicios multicast, aplicables en las redes móviles actuales y futuras. • En el marco de LTE/LTE-A, el eMBMS comparte los recursos de la capa física con las transmisiones unicast (al menos hasta la revisión 12). Por lo tanto, la disponibilidad temporal de las transmisiones multicast está limitada a un máximo del 60 por ciento, reservándose las subtramas restantes (al menos el 40 por ciento) para las transmisiones unicast. Con el objetivo de alcanzar la máxima tasa total de datos (ADR) entre los usuarios multicast, hemos implementado varios esquemas innovadores de SRA que combinan la asignación de los recursos multicast y unicast de la trama LTE/LTE-A, garantizando los requisitos de QoS a cada usuario. • En los sistemas de comunicaciones de banda ancha, la selección del MCS para transmisiones multicast se basa habitualmente en la utilización de CQIs de banda ancha, lo que proporciona información bastante imprecisa acerca de la capacidad potencial del canal multicast. Recientemente se ha empezado a utilizar el CQI por subbanda para mejorar la eficiencia espectral del sistema sin comprometer la robustez de los enlaces. Hemos propuesto nuevas estrategias para SRA multicast basadas en el CQI por subbanda que, basándose en la selección de los modos de transmisión con mayor eficiencia espectral, conducen a mejores tasas de datos, a la vez que permiten cumplir los requisitos de QoS. • Los servicios móviles de vídeo broadcast/multicast precisan estrategias eficientes de SRA con baja complejidad. Hemos propuesto una estrategia de SRA basada en subgrupos multicast y la técnica de codificación de vídeo escalable (SVC) para la difusión de vídeo multicast, la cual se centra en reducir el espacio de búsqueda de soluciones y optimizar el ADR. Los resultados obtenidos en términos de ADR, eficiencia espectral y equidad entre los usuarios multicast, junto con la baja complejidad del algoritmo, ponen de manifiesto que el esquema propuesto es adecuado para su implantación en sistemas reales. Estas contribuciones pretenden servir de referencia que motive la investigación actual y futura en el interesante ámbito de RRM para los servicios broadcast/multicast en las redes móviles de próxima generación.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Atilio Manuel Da Silva Gameiro.- Secretario: Víctor Pedro Gil Jiménez.- Vocal: María de Diego Antó

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research

From MFN to SFN: Performance Prediction Through Machine Learning

In the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97

Demonstrating Immersive Media Delivery on 5G Broadcast and Multicast Testing Networks

This work presents eight demonstrators and one showcase developed within the 5G-Xcast project. They experimentally demonstrate and validate key technical enablers for the future of media delivery, associated with multicast and broadcast communication capabilities in 5th Generation (5G). In 5G-Xcast, three existing testbeds: IRT in Munich (Germany), 5GIC in Surrey (UK), and TUAS in Turku (Finland), have been developed into 5G broadcast and multicast testing networks, which enables us to demonstrate our vision of a converged 5G infrastructure with fixed and mobile accesses and terrestrial broadcast, delivering immersive audio-visual media content. Built upon the improved testing networks, the demonstrators and showcase developed in 5G-Xcast show the impact of the technology developed in the project. Our demonstrations predominantly cover use cases belonging to two verticals: Media & Entertainment and Public Warning, which are future 5G scenarios relevant to multicast and broadcast delivery. In this paper, we present the development of these demonstrators, the showcase, and the testbeds. We also provide key findings from the experiments and demonstrations, which not only validate the technical solutions developed in the project, but also illustrate the potential technical impact of these solutions for broadcasters, content providers, operators, and other industries interested in the future immersive media delivery.Comment: 16 pages, 22 figures, IEEE Trans. Broadcastin

Multiuser Diversity Management for Multicast/Broadcast Services in 5G and Beyond Networks

The envisaged fifth-generation (5G) and beyond networks represent a paradigm shift for global communications, offering unprecedented breakthroughs in media service delivery with novel capabilities and use cases. Addressing the critical research verticals and challenges that characterize the International Mobile Telecommunications (IMT)-2030 framework requires a compelling mix of enabling radio access technologies (RAT) and native softwarized, disaggregated, and intelligent radio access network (RAN) conceptions. In such a context, the multicast/broadcast ser vice (MBS) capability is an appealing feature to address the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. Embracing the MBS capability as a primary component of the envisaged 5G and beyond networks comes with multiple open challenges. In this research, we contextualize and address the necessity of ensuring stringent quality of service (QoS)/quality of experience (QoE) requirements, multicasting over millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies, and handling complex mobility behaviors. In the broad problem space around these three significant challenges, we focus on the specific research problems of effectively handling the trade-off between multicasting gain and multiuser diversity, along with the trade-off between optimal network performance and computational complexity. In this research, we cover essential aspects at the intersection of MBS, radio resource management (RRM), machine learning (ML), and the Open RAN (O-RAN) framework. We characterize and address the dynamic multicast multiuser diversity through low-complexity RRM solutions aided by ML, orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques in 5G MBS and beyond networks. We characterize the performance of the multicast access techniques conventional multicast scheme (CMS), subgrouping based on OMA (S-OMA), and subgrouping based on NOMA (S-NOMA). We provide conditions for their adequate selection regarding the specific network conditions (Chapter 4). Consequently, we propose heuristic methods for the dynamic multicast access technique selection and resource allocation, taking advantage of the multiuser diversity (Chapter 5.1). Moreover, we proposed a multicasting strategy based on fixed pre-computed multiple-input multiple-output (MIMO) multi-beams and S-NOMA (Chapter 5.2). Our approach tackles specific throughput requirements for enabling extended reality (XR) applications attending multiple users and handling their spatial and channel quality diversity. We address the computational complexity (CC) associated with the dynamic multicast RRM strategies and highlight the implications of fast variations in the reception conditions of the multicast group (MG) members. We propose a low complexity ML-based solution structured by a multicast-oriented trigger to avoid overrunning the algorithm, a K-Means clustering for group-oriented detection and splitting, and a classifier for selecting the most suitable multicast access technique (Chapter 6.1). Our proposed approaches allow addressing the trade-off between optimal network performance and CC by maximizing specific QoS parameters through non-optimal solutions, considerably reducing the CC of conventional exhaustive mechanisms. Moreover, we discuss the insertion of ML-based multicasting RRM solutions into the envisioned disaggregated O-RAN framework (Chapter 6.2.5). We analyze specific MBS tasks and the importance of a native decentralized, softwarized, and intelligent conception. We assess the effectiveness of our proposal under multiple numerical and link level simulations of recreated 5G MBS use cases operating in μWave and mmWave. We evaluate various network conditions, service constraints, and users’ mobility behaviors

Recent Advances in Cellular D2D Communications

Device-to-device (D2D) communications have attracted a great deal of attention from researchers in recent years. It is a promising technique for offloading local traffic from cellular base stations by allowing local devices, in physical proximity, to communicate directly with each other. Furthermore, through relaying, D2D is also a promising approach to enhancing service coverage at cell edges or in black spots. However, there are many challenges to realizing the full benefits of D2D. For one, minimizing the interference between legacy cellular and D2D users operating in underlay mode is still an active research issue. With the 5th generation (5G) communication systems expected to be the main data carrier for the Internet-of-Things (IoT) paradigm, the potential role of D2D and its scalability to support massive IoT devices and their machine-centric (as opposed to human-centric) communications need to be investigated. New challenges have also arisen from new enabling technologies for D2D communications, such as non-orthogonal multiple access (NOMA) and blockchain technologies, which call for new solutions to be proposed. This edited book presents a collection of ten chapters, including one review and nine original research works on addressing many of the aforementioned challenges and beyond