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

QoE estimation for Adaptive Video Streaming over LTE Networks

Η 4η γενιά (4G) κινητών επικοινωνιών, στην οποία ανήκει το σύστημα Long Term Evolution (LTE), παρέχει ευρυζωνική πρόσβαση σε κινητές συσκευές με ποιότητα και ταχύτητα που αγγίζουν τις ενσύρματες επικοινωνίες. Παρόλ’αυτά, η κινητικότητα εκ φύσεως εισάγει αστοχίες/διακυμάνσεις στην ασύρματη διεπαφή, γενόντας έτσι την ανάγκη για αντίστοιχη προσαρμογή της ροής μετάδοσης των δεδομένων. Η ανάγκη αυτή είναι ακόμη πιο έκδηλη για τις ροές δεδομένων βίντεο, που έχουν και τη μερίδα του λέοντος στην διαδικτυακή κίνηση. Καθώς, λοιπόν, η ροή βίντεο μέσω ΗΤΤΡ έχει γίνει ο κανόνας στη διανομήπεριεχομένου, η εφαρμογή ενός πρωτοκόλλου προσαρμογής βασισμένου στο HTTP είναι αναπόφευκτη. Το DASH (Dynamic Adaptive Streaming over HTTP) επιτρέπει μια ομαλή, αδιάκοπη ροή video εφαρμόζοντας αλγόριθμους προσαρμογής του bitrate στη μεριά του χρήστη αξιοποιώντας πλήρως την υπάρχουσα υποδομή. Έχοντας ως στόχο να τελειοποιήσουν την ποιότητα την οποία προσφέρει στους χρήστες το δίκτυο, οι ερευνητές συνεχώς αναπτύσσουν νέες φόρμουλες για την εκτίμηση της ποιότητας εμπειρίας του τελικού χρήστη, γνωστής υπο τον όρο Quality of Experience (QoE). Η παρούσα πτυχιακή αντιπροσωπεύει την προσπάθεια συγκερασμού των τριών ακόλουθων πυλώνων: της υποκείμενης υποδομής, του ελέγχου της ποιότητας υπηρεσίας με τη χρήση αλγορίθμων προσαρμογής και του επαναπροσδιορισμού του συστήματος με ανάλυση της ποιότητας και ανατροφοδότηση. Ανοίγει τη συζήτηση για τη χρήση προσαρμοζόμενης ροής μετάδοσης πάνω απο δίκτυα LTE και στοχεύει όχι μόνο να προσφέρει μια βαθιά βιβλιογραφική προσέγγιση των επιμέρους, αλλά και να περιγράψει πώς συνδέονται, πώς επικαλύπτονται, ή πώς αλληλεπιδρούν. Περιγράφει τα σημαντικότερα σύγχρονα μοντέλα μέτρησης QoE και πώς αυτά χρησιμεύουν στην αντικειμενική εκτίμηση της ποιότητας. Βασική συνεισφορά της εργασίας, είναι η ανάπτυξη μιάς πλήρης εκτελέσιμης οντότητας (module) για τον προσομοιωτή NS-3 συνδυάζοντας όλες τις έννοιες που αναφέρονται παραπάνω.Ο αναγνώστης μπορεί να βρεί ενα τυπικό παράδειγμα εκτέλεσης της εν λόγω οντότητας, με την συνοδεία μιας βήμα-βήμα εξήγησής του και και κάποιων διαγραμμάτων με αποτελέσματα. Το NS3 module αναπτύχθηκε με την ελπίδα να φανεί χρήσιμο σε κάθε ερευνητή τηλεπικοινωνιών που ασχολείται με θέματα παροχής ποιότητας εμπειρίας και αναζητά ένα εργαλείο προσομειώσεων.The ability to address an increasing need for mobility in work and entertainment has rendered LTE networks critically essential to our everyday environments. The promising 4th Generation (4G) of Long Term Evolution (LTE) provides ubiquitous broadband access to mobile devices matching land communications in speed and quality. However, the nature of mobility introduces a need for adaptivity in multimedia streaming, the largest part of mobile Internet traffic. As HTTP video streaming has become the de facto dominating solution to distribute media content, the implementation of an HTTP-based adaptive streaming protocol is inevitable. Dynamic Adaptive Streaming over HTTP (DASH) allows for smooth, uninterrupted video streaming by implementing bitrate adaptation algorithms on the client side, with complete utilization of the existing network infrastructure. In order to perfect the current quality served by the network, network researchers constantly develop new metrics to assess the end-user’s Quality of Experience. This thesis represents an attempt to join these three pillars of mobile video streaming: the underlying infrastructure, the over-the-top algorithmic quality control, and the follow-up feedback measurement. It opens a discussion about the use of adaptive streaming in LTE networks, and aims to offer not only a deep down bibliographic approach of each individual concept, but also describe where they overlap, how they connect and interact with each other. It depicts the most important contemporary QoE models and metrics, explains their formulas, and outlines their uses as key performance indicators in objective quality estimation. Furthermore, within this work, we provide a complete, expandable NS-3 model combining all the concepts discussed. An HTTP Server-Client model within the LTE network architecture, with implemented adaptive streaming functionality. The tool was developed in the hope of becoming useful to any telecommunications researcher, supporting their research and introducing them to the NS-3 simulator. In the end, we present a typical execution of our example with a step by step explanation, followed by the plotting of some of the results using a C++ script we developed

SAP: Stall-aware pacing for improved DASH video experience in cellular networks

The dramatic growth of cellular video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent system complexities that degrade streaming performance, such as variability in both video bitrate and network conditions. In this paper, we present SAP as a DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. SAP achieves this by leveraging both network and client state information to optimize the pacing of individual video flows. We extensively evaluate SAP performance using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparison. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric

Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots

Video Caching, Analytics and Delivery at the Wireless Edge: A Survey and Future Directions

Future wireless networks will provide high bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from mobile broadband to the Internet of Things. To this aim, mobile edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 allows improving the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bitrate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined

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

Apport de la Qualité de l’Expérience dans l’optimisation de services multimédia : application à la diffusion de la vidéo et à la VoIP

The emerging and fast growth of multimedia services have created new challenges for network service providers in order to guarantee the best user's Quality of Experience (QoE) in diverse networks with distinctive access technologies. Usually, various methods and techniques are used to predict the user satisfaction level by studying the combined impact of numerous factors. In this thesis, we consider two important multimedia services to evaluate the user perception, which are: video streaming service, and VoIP. This study investigates user's QoE that follows three directions: (1) methodologies for subjective QoE assessment of video services, (2) regulating user's QoE using video a rate adaptive algorithm, and (3) QoE-based power efficient resource allocation methods for Long Term Evaluation-Advanced (LTE-A) for VoIP. Initially, we describe two subjective methods to collect the dataset for assessing the user's QoE. The subjectively collected dataset is used to investigate the influence of different parameters (e.g. QoS, video types, user profile, etc.) on user satisfaction while using the video services. Later, we propose a client-based HTTP rate adaptive video streaming algorithm over TCP protocol to regulate the user's QoE. The proposed method considers three Quality of Service (QoS) parameters that govern the user perception, which are: Bandwidth, Buffer, and dropped Frame rate (BBF). The BBF method dynamically selects the suitable video quality according to network conditions and user's device properties. Lastly, we propose a QoE driven downlink scheduling method, i.e. QoE Power Escient Method (QEPEM) for LTE-A. It esciently allocates the radio resources, and optimizes the use of User Equipment (UE) power utilizing the Discontinuous Reception (DRX) method in LTE-AL'émergence et la croissance rapide des services multimédia dans les réseaux IP ont créé de nouveaux défis pour les fournisseurs de services réseau, qui, au-delà de la Qualité de Service (QoS) issue des paramètres techniques de leur réseau, doivent aussi garantir la meilleure qualité de perception utilisateur (Quality of Experience, QoE) dans des réseaux variés avec différentes technologies d'accès. Habituellement, différentes méthodes et techniques sont utilisées pour prédire le niveau de satisfaction de l'utilisateur, en analysant l'effet combiné de multiples facteurs. Dans cette thèse, nous nous intéressons à la commande du réseau en intégrant à la fois des aspects qualitatifs (perception du niveau de satisfaction de l'usager) et quantitatifs (mesure de paramètres réseau) dans l'objectif de développer des mécanismes capables, à la fois, de s'adapter à la variabilité des mesures collectées et d'améliorer la qualité de perception. Pour ce faire, nous avons étudié le cas de deux services multimédia populaires, qui sont : le streaming vidéo, et la voix sur IP (VoIP). Nous investiguons la QoE utilisateur de ces services selon trois aspects : (1) les méthodologies d'évaluation subjective de la QoE, dans le cadre d'un service vidéo, (2) les techniques d'adaptation de flux vidéo pour garantir un certain niveau de QoE, et (3) les méthodes d'allocation de ressource, tenant compte de la QoE tout en économisant l'énergie, dans le cadre d'un service de VoIP (LTE-A). Nous présentons d'abord deux méthodes pour récolter des jeux de données relatifs à la QoE. Nous utilisons ensuite ces jeux de données (issus des campagnes d'évaluation subjective que nous avons menées) pour comprendre l'influence de différents paramètres (réseau, terminal, profil utilisateur) sur la perception d'un utilisateur d'un service vidéo. Nous proposons ensuite un algorithme de streaming vidéo adaptatif, implémenté dans un client HTTP, et dont le but est d'assurer un certain niveau de QoE et le comparons à l'état de l'art. Notre algorithme tient compte de trois paramètres de QoS (bande passante, taille de mémoires tampons de réception et taux de pertes de paquets) et sélectionne dynamiquement la qualité vidéo appropriée en fonction des conditions du réseau et des propriétés du terminal de l'utilisateur. Enfin, nous proposons QEPEM (QoE Power Efficient Method), un algorithme d'ordonnancement basé sur la QoE, dans le cadre d'un réseau sans fil LTE, en nous intéressant à une allocation dynamique des ressources radio en tenant compte de la consommation énergétiqu

QoE on media deliveriy in 5G environments

231 p.5G expandirá las redes móviles con un mayor ancho de banda, menor latencia y la capacidad de proveer conectividad de forma masiva y sin fallos. Los usuarios de servicios multimedia esperan una experiencia de reproducción multimedia fluida que se adapte de forma dinámica a los intereses del usuario y a su contexto de movilidad. Sin embargo, la red, adoptando una posición neutral, no ayuda a fortalecer los parámetros que inciden en la calidad de experiencia. En consecuencia, las soluciones diseñadas para realizar un envío de tráfico multimedia de forma dinámica y eficiente cobran un especial interés. Para mejorar la calidad de la experiencia de servicios multimedia en entornos 5G la investigación llevada a cabo en esta tesis ha diseñado un sistema múltiple, basado en cuatro contribuciones.El primer mecanismo, SaW, crea una granja elástica de recursos de computación que ejecutan tareas de análisis multimedia. Los resultados confirman la competitividad de este enfoque respecto a granjas de servidores. El segundo mecanismo, LAMB-DASH, elige la calidad en el reproductor multimedia con un diseño que requiere una baja complejidad de procesamiento. Las pruebas concluyen su habilidad para mejorar la estabilidad, consistencia y uniformidad de la calidad de experiencia entre los clientes que comparten una celda de red. El tercer mecanismo, MEC4FAIR, explota las capacidades 5G de analizar métricas del envío de los diferentes flujos. Los resultados muestran cómo habilita al servicio a coordinar a los diferentes clientes en la celda para mejorar la calidad del servicio. El cuarto mecanismo, CogNet, sirve para provisionar recursos de red y configurar una topología capaz de conmutar una demanda estimada y garantizar unas cotas de calidad del servicio. En este caso, los resultados arrojan una mayor precisión cuando la demanda de un servicio es mayor