QOE-AWARE CONTENT DISTRIBUTION SYSTEMS FOR ADAPTIVE BITRATE VIDEO STREAMING

A prodigious increase in video streaming content along with a simultaneous rise in end system capabilities has led to the proliferation of adaptive bit rate video streaming users in the Internet. Today, video streaming services range from Video-on-Demand services like traditional IP TV to more recent technologies such as immersive 3D experiences for live sports events. In order to meet the demands of these services, the multimedia and networking research community continues to strive toward efficiently delivering high quality content across the Internet while also trying to minimize content storage and delivery costs. The introduction of flexible and adaptable technologies such as compute and storage clouds, Network Function Virtualization and Software Defined Networking continue to fuel content provider revenue. Today, content providers such as Google and Facebook build their own Software-Defined WANs to efficiently serve millions of users worldwide, while NetFlix partners with ISPs such as ATT (using OpenConnect) and cloud providers such as Amazon EC2 to serve their content and manage the delivery of several petabytes of high-quality video content for millions of subscribers at a global scale, respectively. In recent years, the unprecedented growth of video traffic in the Internet has seen several innovative systems such as Software Defined Networks and Information Centric Networks as well as inventive protocols such as QUIC, in an effort to keep up with the effects of this remarkable growth. While most existing systems continue to sub-optimally satisfy user requirements, future video streaming systems will require optimal management of storage and bandwidth resources that are several orders of magnitude larger than what is implemented today. Moreover, Quality-of-Experience metrics are becoming increasingly fine-grained in order to accurately quantify diverse content and consumer needs. In this dissertation, we design and investigate innovative adaptive bit rate video streaming systems and analyze the implications of recent technologies on traditional streaming approaches using real-world experimentation methods. We provide useful insights for current and future content distribution network administrators to tackle Quality-of-Experience dilemmas and serve high quality video content to several users at a global scale. In order to show how Quality-of-Experience can benefit from core network architectural modifications, we design and evaluate prototypes for video streaming in Information Centric Networks and Software-Defined Networks. We also present a real-world, in-depth analysis of adaptive bitrate video streaming over protocols such as QUIC and MPQUIC to show how end-to-end protocol innovation can contribute to substantial Quality-of-Experience benefits for adaptive bit rate video streaming systems. We investigate a cross-layer approach based on QUIC and observe that application layer-based information can be successfully used to determine transport layer parameters for ABR streaming applications

Quality of experience-centric management of adaptive video streaming services : status and challenges

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Network-aware video streaming for future media Internet

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QoE management of multimedia streaming services in future networks : a tutorial and survey

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Architectures and Algorithms for Content Delivery in Future Networks

Traditional Content Delivery Networks (CDNs) built with traditional Internet technology are less and less able to cope with today’s tremendous content growth. Enhancing infrastructures with storage and computation capabilities may help to remedy the situation. Information-Centric Networks (ICNs), a proposed future Internet technology, unlike the current Internet, decouple information from its sources and provide in-network storage. However, content delivery over in-network storage-enabled networks still faces significant issues, such as the stability and accuracy of estimated bitrate when using Dynamic Adaptive Streaming (DASH). Still Implementing new infrastructures with in-network storage can lead to other challenges. For instance, the extensive deployment of such networks will require a significant upgrade of the installed IP infrastructure. Furthermore, network slicing enables services and applications with very different characteristics to co-exist on the same network infrastructure. Another challenge is that traditional architectures cannot meet future expectations for streaming in terms of latency and network load when it comes to content, such as 360° videos and immersive services. In-Network Computing (INC), also known as Computing in the Network (COIN), allows the computation tasks to be distributed across the network instead of being computed on servers to guarantee performance. INC is expected to provide lower latency, lower network traffic, and higher throughput. Implementing infrastructures with in-network computing will help fulfill specific requirements for streaming 360° video streaming in the future. Therefore, the delivery of 360° video and immersive services can benefit from INC. This thesis elaborates and addresses the key architectural and algorithmic research challenges related to content delivery in future networks. To tackle the first challenge, we propose algorithms for solving the inaccuracy of rate estimation for future CDNs implementation with in-network storage (a key feature of future networks). An algorithm for implementing in-network storage in IP settings for CDNs is proposed for the second challenge. Finally, for the third challenge, we propose an architecture for provisioning INC-enabled slices for 360° video streaming in next-generation networks. We considered a P4-enabled Software-Defined network (SDN) as the physical infrastructure and significantly reduced latency and traffic load for video streaming

Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness

Mobile Oriented Future Internet (MOFI)

This Special Issue consists of seven papers that discuss how to enhance mobility management and its associated performance in the mobile-oriented future Internet (MOFI) environment. The first two papers deal with the architectural design and experimentation of mobility management schemes, in which new schemes are proposed and real-world testbed experimentations are performed. The subsequent three papers focus on the use of software-defined networks (SDN) for effective service provisioning in the MOFI environment, together with real-world practices and testbed experimentations. The remaining two papers discuss the network engineering issues in newly emerging mobile networks, such as flying ad-hoc networks (FANET) and connected vehicular networks

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

Cache-Aware Adaptive Video Streaming in 5G networks

Η τεχνολογία προσαρμοστικής ροής video μέσω HTTP έχει επικρατήσει ως ο κυρίαρχος τρόπος μετάδοσης video στο Internet. Η τεχνολογία αυτή βασίζεται στη λήψη μικρών διαδοχικών τμημάτων video από έναν server. Μία πρόκληση που όμως δεν έχει διερευνηθεί επαρκώς είναι η λήψη τμημάτων video από περισσότερους από έναν servers, με τρόπο που να εξυπηρετεί τόσο τις ανάγκες του δικτύου όσο και τη βελτίωση της Ποιότητας Εμπειρίας του χρήστη (Quality of Experience, QoE). Η συγκεκριμένη διπλωματική εργασία θα διερευνήσει αυτό το πρόβλημα, προσομοιώνοντας ένα δίκτυο με πολλαπλούς video servers και διάφορους video clients. Στη συνέχεια, θα υλοποιήσει τόσο την δυνατότητα επικοινωνίας peer-to-many στα πλαίσια της προσαρμοστικής ροής video όσο και τον αλγόριθμο επιλογής video server. Όλα αυτά θα διερευνηθούν στο περιβάλλον του Mininet, που είναι ένας δικτυακός εξομοιωτής, για να προσομοιωθεί η τεχνολογία DASH με τη βοήθεια των κόμβων του δικτύου του εξομοιωτή. Αρχικά, το βίντεο χωρίστηκε σε μικρά κομμάτια με τη βοήθεια του εργαλείου ffmpeg και στη συνέχεια, υλοποιήθηκαν πειράματα που ένας πελάτης ζητούσε το βίντεο από έναν server προσωρινής αποθήκευσης (cache server). Αν το συγκεκριμένο τμήμα του βίντεο δεν υπήρχε εκεί, τότε στελνόταν αίτημα από τον server προσωρινής αποθήκευσης σε έναν διακομιστή που περιείχε όλα τα τμήματα του βίντεο (main server). Στα πειράματα αυτά εξετάστηκε και η προστιθέμενη δικτυακή κίνηση, με τελικό συμπέρασμα ότι το περιβάλλον του Mininet προκαλεί αναπόφευκτους περιορισμούς στη περίπτωση της δικτυακής κίνησης, καθώς παρατηρήσαμε πως το κανάλι του server βάσης δεδομένων παρέμενε ανενεργό καθ’ όλη τη διάρκεια αιτημάτων από τον server προσωρινής αποθήκευσης, με αποτέλεσμα να δημιουργούνται συνθήκες μη-ρεαλιστικού δικτύου. Γι’ αυτόν τον λόγο, προβήκαμε στην υλοποίηση μιας νέας προσέγγισης, εξαλείφοντας το Mininet περιβάλλον και δουλεύοντας πάνω σε νέες τεχνικές προσθήκης δικτυακής κίνησης και τροποποιώντας την επικοινωνία των διακομιστών μεταξύ τους. Με αυτόν τον τρόπο, καταφέραμε να δείξουμε σαφέστερα τους περιορισμούς της προηγούμενης προσέγγισης αλλά και να συμπεράνουμε ότι η ύπαρξη servers προσωρινής αποθήκευσης είναι ένα χρήσιμο εργαλείο υπό όρους αύξησης της ποιότητας εμπειρίας ενός χρήστη. Η γενική τάση που παρατηρήθηκε ήταν ότι με την αύξηση του διαθέσιμου χώρου αποθήκευσης, η ποιότητα αναπαραγωγής του βίντεο ανέβαινε σε κάποιο βαθμό. Ταυτόχρονα όμως, το ποσοστό βελτίωσης αυτό, είναι άρρηκτα δεμένο με τον αλγόριθμο επιλογής κομματιών βίντεο που χρησιμοποιείται. Για ακόμα καλύτερα αποτελέσματα λοιπόν, θεωρείται αναγκαία η εύρεση της χρυσής τομής μεταξύ χωρητικότητας του χώρου προσωρινής αποθήκευσης και αλγορίθμου επιλογής κομματιών. Στην παρούσα διπλωματική παρουσιάζονται τα εξής κεφάλαια: Στο κεφάλαιο 1 αναφέρεται η ιστορική αναδρομή της τεχνολογίας των δικτύων. Στο κεφάλαιο 2 αναλύεται η τεχνολογία προσαρμοστικής ροής βίντεο μέσω HTTP. Στο κεφάλαιο 3 αναλύονται οι διαφορετικές τεχνικές προσωρινής αποθήκευσης. Στο κεφάλαιο 4 παρουσιάζεται η έννοια της Ποιότητας Εμπειρίας του χρήστη και η συσχέτισή της με πολλούς άλλους παράγοντες. Το κεφάλαιο 5 περιγράφεται αναλυτικά η διαδικασία στησίματος του περιβάλλοντος και τα διάφορα απαραίτητα εργαλεία για την υλοποίησή μας. Το κεφάλαιο 6 αναφέρει τα πειράματα μέσω Mininet, την τοπολογία και όλο το στήσιμο, καθώς και τους λόγους που μας οδήγησαν στην πορεία μιας διαφορετικής προσέγγισης. Στο κεφάλαιο 7 προτείνεται η διαφορετική προσέγγιση και παρουσιάζεται η μεθοδολογία και οι μετρικές. Επίσης, αναλύονται διαγράμματα που εξάχθηκαν από την ανάλυση τω μετρικών. Τέλος, το κεφάλαιο 8 αφορά τα συμπεράσματα και θέματα μελλοντικής έρευνας για βελτίωση της Ποιότητας Εμπειρίας του χρήστη περαιτέρω.Dynamic Adaptive Streaming over HTTP (DASH) has prevailed as the dominant way of video transmission over the Internet. This technology is based on receiving small sequential video segments from a server. However, one challenge that has not been adequately examined, is the obtainment of video segments from more than one server, in a way that serves both the needs of the network and the improvement of the Quality of Experience (QoE). This thesis will investigate this problem by simulating a network with multiple video servers and a video client. It will then implement both the peer-to-many communication in the context of adaptive video streaming and the video server caching algorithm based on proposed criteria that will improve the status of the network and/or the user. All of this will be explored in the environment of Mininet, which is a network emulator, in order to simulate the DASH technology with the help of the emulator network nodes. Initially, the video was split into small segments using the ffmpeg tool, and then experiments were conducted in which a client requested the video from a cache server. If the segment could not be found in the cache server, then a request was sent from the cache server to a server that contained all segments of the video (main server). In these experiments, the added traffic was also examined, by concluded to the fact that the Mininet environment causes unavoidable limitations in the case of the traffic. What we observed was that the main server channel remained inactive throughout the requests of the cache server, resulting in unrealistic network conditions. For this reason, we have explored a new approach, eliminating the Mininet environment and working on new techniques for adding web traffic and modifying the communication of the servers, regarding the requests they receive. In this way, we were able to clearly show the limitations of the previous approach but also to conclude that the existence of caching servers is a useful tool in terms of increasing the quality of experience. The general tendency was that, as the available buffer size increased, the video playback quality increased to some extent. However, at the same time this improvement is linked to the random selection algorithm. For even better results, it is considered necessary to find an appropriate caching selection algorithm in order to take full advantage of the caching technology. The following chapters presented in this thesis are: Chapter 1 mentions the historical background of the networks. Chapter 2 analyzes the Dynamic Adaptive Streaming over HTTP. Chapter 3 analyzes the caching techniques. Chapter 4 presents the concept of Quality of Experience and its correlation with many other factors. Chapter 5 describes in detail the process of setting up the environment and the various necessary tools for our implementation. Chapter 6 refers to the Mininet experiments, the topology, and the set-up, as well as the reasons that led us to a different approach. Chapter 7 proposes the different approach and presents the methodology and the metrics. Also, diagrams extracted from the analysis of the metrics are analyzed in Chapter 7. Finally, Chapter 8 summarizes the conclusions and issues of future research to improve the Quality of Experience even further