CLEVER: a cooperative and cross-layer approach to video streaming in HetNets

We investigate the problem of providing a video streaming service to mobile users in an heterogeneous cellular network composed of micro e-NodeBs (eNBs) and macro e-NodeBs (MeNBs). More in detail, we target a cross-layer dynamic allocation of the bandwidth resources available over a set of eNBs and one MeNB, with the goal of reducing the delay per chunk experienced by users. After optimally formulating the problem of minimizing the chunk delay, we detail the Cross LayEr Video stReaming (CLEVER) algorithm, to practically tackle it. CLEVER makes allocation decisions on the basis of information retrieved from the application layer aswell as from lower layers. Results, obtained over two representative case studies, show that CLEVER is able to limit the chunk delay, while also reducing the amount of bandwidth reserved for offloaded users on the MeNB, as well as the number of offloaded users. In addition, we show that CLEVER performs clearly better than two selected reference algorithms, while being very close to a best bound. Finally, we show that our solution is able to achieve high fairness indexes and good levels of Quality of Experience (QoE)

Enabling energy-awareness for internet video

Continuous improvements to the state of the art have made it easier to create, send and receive vast quantities of video over the Internet. Catalysed by these developments, video is now the largest, and fastest growing type of traffic on modern IP networks. In 2015, video was responsible for 70% of all traffic on the Internet, with an compound annual growth rate of 27%. On the other hand, concerns about the growing energy consumption of ICT in general, continue to rise. It is not surprising that there is a significant energy cost associated with these extensive video usage patterns. In this thesis, I examine the energy consumption of typical video configurations during decoding (playback) and encoding through empirical measurements on an experimental test-bed. I then make extrapolations to a global scale to show the opportunity for significant energy savings, achievable by simple modifications to these video configurations. Based on insights gained from these measurements, I propose a novel, energy-aware Quality of Experience (QoE) metric for digital video - the Energy - Video Quality Index (EnVI). Then, I present and evaluate vEQ-benchmark, a benchmarking and measurement tool for the purpose of generating EnVI scores. The tool enables fine-grained resource-usage analyses on video playback systems, and facilitates the creation of statistical models of power usage for these systems. I propose GreenDASH, an energy-aware extension of the existing Dynamic Adaptive Streaming over HTTP standard (DASH). GreenDASH incorporates relevant energy-usage and video quality information into the existing standard. It could enable dynamic, energy-aware adaptation for video in response to energy-usage and user ‘green’ preferences. I also evaluate the subjective perception of such energy-aware, adaptive video streaming by means of a user study featuring 36 participants. I examine how video may be adapted to save energy without a significant impact on the Quality of Experience of these users. In summary, this thesis highlights the significant opportunities for energy savings if Internet users gain an awareness about their energy usage, and presents a technical discussion how this can be achieved by straightforward extensions to the current state of the art

Measuring and Improving the Quality of Experience of Adaptive Rate Video

Today's popular over-the-top (OTT) video streaming services such as YouTube, Netflix and Hulu deliver video contents to viewers using adaptive bitrate (ABR) technologies. In ABR streaming, a video player running on a viewer's device adaptively changes bitrates to match given network conditions. However, providing reliable streaming is challenging. First, an ABR player may select an inappropriate bitrate during playback due to the lack of direct knowledge of access networks, frequent user mobility and rapidly changing channel conditions. Second, OTT content is delivered to viewers without any cooperation with Internet service providers (ISPs). Last, there are no appropriate tools that evaluate the performance of ABR streaming along with video quality of experience (QoE). This thesis describes how to improve the video QoE of OTT video streaming services using ABR technologies. Our analysis starts from understanding ABR heuristics. How does ABR streaming work? What factors does an ABR player consider when switching bitrates during a download? Then, we propose our solutions to improve existing ABR streaming from the perspective of network operators who deliver video content through their networks and video service providers who build ABR players running on viewers' devices. From the network operators' point of view, we propose to find a better video content server based on round trip times (RTTs) between an edge node of a wireless network and available video content servers when a viewer requests a video. The edge node can be an Internet Service Provider (ISP) router in a Wi-Fi network and a packet data network gateway (P-GW) in a 4G network. During the experiments, our solution showed better TCP performance (e.g., higher TCP throughput during playback) 146 times out of 200 experiments (73%) over Wi-Fi networks and 162 times out of 200 experiments (81%) over 3G networks. In addition, we claim that the wireless edge nodes can assist an ABR video player in selecting the best available bitrate by controlling the available bandwidth in the radio access network between a base station and a viewer's device. In our Wi-Fi testbed, the proposed solution saved up to 21% of radio bandwidth on mobile devices and enhanced the viewing experience by reducing rebufferings during playback. Last, we assert that software-defined networking (SDN) can improve video QoE by dynamically controlling routing paths of video streaming flows based on the provisioned networking information collected from SDN-enabled networking devices. Using an off-the-shelf SDN platform, we showed that our proposed solution can reduce rebufferings by 50% and provide higher bitrates during a download. From the perspective of video service providers, higher video QoE can be achieved by improving ABR heuristics implemented in an ABR player. To support this idea, we investigated the role of playout buffer size in ABR streaming and its impact on video QoE. Through our video QoE survey, we proved that a large buffer does not always outperform a small buffer, especially under rapidly varying network conditions. Based on this finding, we suggest to dynamically change the maximum buffer size in an ABR player depending on the current capacity of its playout buffer for improving the QoE of viewers. During the experiments, our proposed solution improved the viewing experience by offering 15% higher average played bitrate, 70% fewer bitrate changes and 50% shorter rebuffering duration. Our experimental results show that even small changes of ABR heuristics and new features of network systems can greatly affect video QoE. However, it is still difficult for video service providers or network operators to evaluate new ABR heuristics or network system changes due to lack of accurate QoE monitoring systems. In order to solve this issue, we have developed YouSlow ("YouTube Too Slow!? - YouSlow") as a new approach to monitoring video QoE for the analysis of ABR performance. The lightweight web browser plug-in and mobile application are designed to monitor various playback events (e.g., rebuffering duration and frequency of bitrate changes) directly from within ABR video players and calculate statistics along with video QoE. Using YouSlow, we investigate the impact of the above playback events on video abandonment: about 10% of viewers abandoned the YouTube videos when the pre-roll ads lasted for 15 seconds. Even increasing the bitrate can annoy viewers; they prefer a high starting bitrate with no bitrate changes during playback. Our regression analysis shows that bitrate changes do not affect video abandonment significantly and the abandonment rate can be estimated accurately using the rebuffering ratio and the number of rebufferings. The thesis includes four main contributions. First, we investigate today's popular OTT video streaming services (e.g., YouTube and Netflix) that use ABR streaming technologies. Second, we propose to build QoS and QoE aware video streaming that can be implemented in existing wireless networks (e.g., Wi-Fi, 3G and 4G) and in SDN-enabled networks. Third, we propose to improve current ABR heuristics by dynamically changing the playout buffer size under varying network conditions. Last, we designed and implemented a new monitoring system for measuring video QoE

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

SLS: Smart localization service: human mobility models and machine learning enhancements for mobile phone’s localization

In recent years we are witnessing a noticeable increment in the usage of new generation smartphones, as well as the growth of mobile application development. Today, there is an app for almost everything we need. We are surrounded by a huge number of proactive applications, which automatically provide relevant information and services when and where we need them. This switch from the previous generation of passive applications to the new one of proactive applications has been enabled by the exploitation of context information. One of the most important and most widely used pieces of context information is location data. For this reason, new generation devices include a localization engine that exploits various embedded technologies (e.g., GPS, WiFi, GSM) to retrieve location information. Consequently, the key issue in localization is now the efficient use of the mobile localization engine, where efficient means lightweight on device resource consumption, responsive, accurate and safe in terms of privacy. In fact, since the device resources are limited, all the services running on it have to manage their trade-off between consumption and reliability to prevent a premature depletion of the phone’s battery. In turn, localization is one of the most demanding services in terms of resource consumption. In this dissertation I present an efficient localization solution that includes, in addition to the standard location tracking techniques, the support of other technologies already available on smartphones (e.g., embedded sensors), as well as the integration of both Human Mobility Modelling (HMM) and Machine Learning (ML) techniques. The main goal of the proposed solution is the provision of a continuous tracking service while achieving a sizeable reduction of the energy impact of the localization with respect to standard solutions, as well as the preservation of user privacy by avoiding the use of a back-end server. This results in a Smart Localization Service (SLS), which outperforms current solutions implemented on smartphones in terms of energy consumption (and, therefore, mobile device lifetime), availability of location information, and network traffic volume

MediaSync: Handbook on Multimedia Synchronization

This book provides an approachable overview of the most recent advances in the fascinating field of media synchronization (mediasync), gathering contributions from the most representative and influential experts. Understanding the challenges of this field in the current multi-sensory, multi-device, and multi-protocol world is not an easy task. The book revisits the foundations of mediasync, including theoretical frameworks and models, highlights ongoing research efforts, like hybrid broadband broadcast (HBB) delivery and users' perception modeling (i.e., Quality of Experience or QoE), and paves the way for the future (e.g., towards the deployment of multi-sensory and ultra-realistic experiences). Although many advances around mediasync have been devised and deployed, this area of research is getting renewed attention to overcome remaining challenges in the next-generation (heterogeneous and ubiquitous) media ecosystem. Given the significant advances in this research area, its current relevance and the multiple disciplines it involves, the availability of a reference book on mediasync becomes necessary. This book fills the gap in this context. In particular, it addresses key aspects and reviews the most relevant contributions within the mediasync research space, from different perspectives. Mediasync: Handbook on Multimedia Synchronization is the perfect companion for scholars and practitioners that want to acquire strong knowledge about this research area, and also approach the challenges behind ensuring the best mediated experiences, by providing the adequate synchronization between the media elements that constitute these experiences

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

Quality of experience characterization and provisioning in mobile cellular networks

Παραδοσιακά, οι προηγούμενες γενεές κινητών κυψελωτών δικτύων έχουν σχεδιαστεί με κριτήρια Ποιότητας Υπηρεσίας, έτσι ώστε να πληρούν συγκεκριμένες απαιτήσεις διαφόρων υπηρεσιών. Η «Ποιότητα Εμπειρίας» έχει, ωστόσο, πρόσφατα εμφανιστεί ως έννοια, επηρεάζοντας το σχεδιασμό των μελλοντικών γενεών των δικτύων, δίνοντας σαφή έμφαση στην πραγματικά επιτευχθείσα εμπειρία του τελικού χρήστη. Η εμφάνιση της έννοιας της Ποιότητας Εμπειρίας οφείλεται στην αναπόφευκτη, ισχυρή μετάβαση που βιώνει η βιομηχανία των Τηλεπικοινωνιών από συστημο-κεντρικά δίκτυα σε πιο χρηστο-κεντρικές λύσεις και στόχους. Οι πάροχοι κινητών δικτύων, οι πάροχοι υπηρεσιών, οι προγραμματιστές εφαρμογών, αλλά και άλλα ενδιαφερόμενα μέλη που εμπλέκονται στην αλυσίδα παροχής υπηρεσιών προσελκύονται από τις ευκαιρίες που μπορεί να προσφέρει η ενσωμάτωση γνώσης Ποιότητας Εμπειρίας στο επιχειρηματικό τους μοντέλο. Πράγματι, η παρεχόμενη Ποιότητα Εμπειρίας αποτελεί έναν καθοριστικό παράγοντα διαφοροποίησης μεταξύ των διαφόρων παικτών, μία τάση που αναμένεται να γίνει ακόμη πιο έντονη τα επόμενα χρόνια. Υποκινούμενη από αυτή την χρηστο-κεντρική τάση, η έρευνα που διεξάγεται σε αυτή τη διατριβή έχει ως στόχο την διερεύνηση των προκλήσεων και των ευκαιριών που προκύπτουν στα σύγχρονα κινητά κυψελωτά δίκτυα όταν λαμβάνεται υπόψιν η έννοια της Ποιότητας Εμπειρίας. Τέτοιες ευκαιρίες αφορούν, καταρχήν, τη δυνατότητα κατανόησης της Ποιότητας Εμπειρίας που επιτυγχάνει ένας πάροχος κατά την προσφορά μίας υπηρεσίας. Αυτό μπορεί να επιτευχθεί με την υλοποίηση και ενσωμάτωση μεθόδων αξιολόγησης Ποιότητας Εμπειρίας στην πραγματικού-χρόνου λειτουργία ενός δικτύου. Εν συνεχεία, ακολουθεί η εκμετάλλευση της συλλεγμένης ευφυΐας που σχετίζεται με την Ποιότητα Εμπειρίας, προκειμένου να επανεξεταστούν υφιστάμενοι μηχανισμοί επιπέδου δικτύου (π.χ., χρονο-προγραμματισμός ραδιοπόρων) ή μηχανισμοί επιπέδου εφαρμογής (π.χ., ροή βίντεο), αλλά και να προταθούν καινοτόμες διαστρωματικές προσεγγίσεις προς όφελος της Ποιότητας Εμπειρίας. Επιπλέον, υπάρχει η δυνατότητα πρότασης νέων αλγορίθμων που προκύπτουν από τα εγγενή χαρακτηριστικά της Ποιότητας Εμπειρίας, όπως η μη γραμμική επίδραση μετρικών Ποιότητας Υπηρεσίας στην Ποιότητα Εμπειρίας, με στόχο την περαιτέρω βελτίωσή της. Σε αυτή την κατεύθυνση, στην παρούσα διατριβή, διερευνώνται και αξιοποιούνται μοντέλα και μετρικές εκτίμησης Ποιότητας Εμπειρίας με στόχο την ποσοτικοποίησή της, έχοντας ως απώτερο στόχο την εισαγωγή βελτιώσεων στους υφιστάμενους μηχανισμούς κινητών κυψελωτών δικτύων. Ο πυρήνας αυτής της διατριβής είναι η πρόταση μίας κυκλικής διεργασίας παροχής Ποιότητας Εμπειρίας που επιτρέπει τον έλεγχο, την παρακολούθηση (ήτοι, τη μοντελοποίηση) και τη διαχείριση της Ποιότητας Εμπειρίας σε ένα κυψελωτό δίκτυο. Κάθε μία από αυτές τις λειτουργίες αναλύεται περαιτέρω, ενώ έμφαση δίνεται στις λειτουργίες μοντελοποίησης και διαχείρισης. Όσον αφορά τη μοντελοποίηση, γίνεται περιγραφή και ταξινόμηση των μεθόδων εκτίμησης και των δεικτών επιδόσεων Ποιότητας Εμπειρίας. Η παραμετρική εκτίμηση της ποιότητας αναδεικνύεται ως η πιο ελκυστική κατηγορία μοντελοποίησης Ποιότητας Εμπειρίας σε κινητά κυψελωτά δίκτυα, οπότε και περιγράφεται διεξοδικά για ευρέως χρησιμοποιούμενους τύπους υπηρεσιών, όπως η συνομιλία (φωνή) μέσω Internet Protocol (IP) και η μετάδοση βίντεο. Όσον αφορά τη διαχείριση Ποιότητας Εμπειρίας, προτείνονται νέοι μηχανισμοί που επιδεικνύουν βελτιώσεις στην εμπειρία των τελικών χρηστών, και συγκεκριμένα: α) ένα σχήμα ελέγχου των επικοινωνιών συσκευής-προς-συσκευή που λαμβάνει υπόψιν την εμπειρία των χρηστών, β) ένας «συνεπής» αλγόριθμος χρονο-προγραμματισμού ραδιοπόρων που βελτιώνει την Ποιότητα Εμπειρίας του χρήστη μετριάζοντας τις διακυμάνσεις της ρυθμαπόδοσης του δικτύου, και γ) ένας μηχανισμός προσαρμοστικής ροής βίντεο με γνώσεις «πλαισίου», ο οποίος επιτυγχάνει την εξάλειψη διακοπών του βίντεο σε συνθήκες χαμηλού εύρους ζώνης. Επιπλέον, προτείνεται μία εφαρμογή Ποιότητας Εμπειρίας βασισμένη στην αρχιτεκτονική Software-Defined Networking (SDN), ονόματι “QoE-SDN APP”, η οποία επιτρέπει την ανάδραση πληροφοριών δικτύου από παρόχους κινητής τηλεφωνίας σε παρόχους υπηρεσιών βίντεο, αναδεικνύοντας πλεονεκτήματα ως προς την Ποιότητα Εμπειρίας για τους πελάτες των παρόχων βίντεο αλλά και ως προς την εξοικονόμηση εύρους ζώνης για τους φορείς εκμετάλλευσης δικτύου. Εν κατακλείδι, η παρούσα διατριβή προωθεί την ενοποίηση του ερευνητικού πεδίου της Ποιότητας Εμπειρίας με τον τομέα των κινητών επικοινωνιών, καθώς και τη συνεργασία αμοιβαίου ενδιαφέροντος μεταξύ των παρόχων δικτύου (επίπεδο δικτύου) με τους παρόχους υπηρεσιών (επίπεδο εφαρμογής), αναδεικνύοντας την δυναμική από τέτοιου είδους προσεγγίσεις για όλους τους εμπλεκόμενους φορείς.Traditionally, previous generations of mobile cellular networks have been designed with Quality of Service (QoS) criteria in mind, so that they manage to meet specific service requirements. Quality of Experience (QoE) has, however, recently emerged as a concept, disrupting the design of future network generations by giving clear emphasis on the actually achieved user experience. The emergence of the QoE concept has been a result of the inevitable strong transition that the Telecom industry is currently experiencing from system-centric networks to more user-centric solutions and objectives. Mobile network operators, service providers, application developers, as well as other stakeholders involved in the service provisioning chain have been attracted by the opportunities that the integration of the QoE concept could bring to their business; indeed, the provisioned QoE constitutes a determining factor of differentiation among different stakeholders, a tendency which is expected to become even more intense in the years to come. Motivated by this boost towards user-centricity, the objective of the research conducted in this thesis is to explore the challenges and opportunities that arise in modern mobile cellular networks when QoE is considered. Such opportunities concern, first of all, the possibility to comprehend the QoE that a provider achieves when provisioning a service. This can be enabled by the implementation and integration of QoE assessment methods into the real-time operation of a network. Then, the next step is the exploitation of collected QoE-related intelligence in order to re-examine existing network-layer mechanisms (e.g., radio scheduling), or application-layer mechanisms (e.g., video streaming), as well as propose novel cross-layer approaches towards ameliorating the achieved QoE. Moreover, the opportunity emerges to propose novel algorithms that stem from the inherent idiosyncrasies of QoE, such as the non-linear impact of QoS-related parameters on QoE, as a way to further enhance the users’ QoE. In this direction, throughout this thesis, QoE estimation models and metrics are explored and exploited in order to quantify QoE and thus, to improve existing mechanisms of mobile cellular networks. The core of this thesis is the proposal of a QoE provisioning cycle that allows the control, monitoring (i.e., modeling) and management of QoE in a cellular network. Each one of these functions is further analyzed, while emphasis is given on the modeling and management operations. In terms of modeling, QoE assessment methods and QoE-related performance indicators are described and classified. Parametric quality estimation is identified as the most appealing type of QoE estimation in mobile cellular networks, thus, it is thoroughly described for widely used types of services, such as Voice over IP (VoIP) and video streaming. In terms of QoE management, novel QoE-aware mechanisms that demonstrate QoE improvements for the users are proposed, namely: a) a QoE-driven Device-to-Device (D2D) communication management scheme that enhances end-user QoE, b) a “consistent” radio scheduling algorithm that improves the end-user QoE by mitigating throughput fluctuations, and c) a context-aware HTTP Adaptive Streaming (HAS) mechanism that successfully mitigates stallings (i.e., video freezing events) in the context of bandwidth-challenging scenarios. Moreover, a programmable QoE-SDN APP into the Software-Defined Networking (SDN) architecture is introduced, which enables network feedback exposure from mobile network operators to video service providers, revealing QoE benefits for the customers of video providers and bandwidth savings for the network operators. Overall, this thesis promotes the uniting of the domain of QoE with the domain of mobile communications, as well as the collaboration of mutual-interest between mobile network operators (network layer) and service providers (application layer), presenting the high potential from such approaches for all involved stakeholders