Ensuring QoE in contemporary mobile networks for video content distribution

A framework that aims to best utilize the mobile network resources for video applications is presented in this paper. The main contribution of the work proposed is the QoE-driven optimization method that can maintain a desired trade-off between fairness and efficiency in allocating resources in terms of data rates to video streaming users in LTE networks. This method is concerned with the control of the user satisfaction level from the service continuity's point of view and applies appropriate QoE metrics (Pause Intensity and variations) to determine the scheduling strategies in combination with the mechanisms used for adaptive video streaming such as 3GP/MPEG-DASH. The superiority of the proposed algorithms are demonstrated, showing how the resources of a mobile network can be optimally utilized by using quantifiable QoE measurements. This approach can also find the best match between demand and supply in the process of network resource distribution

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)

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

Smart Television Services Using NFV/SDN Network Management

International audienceIntegrating joint network function virtualization (NFV) and software-defined networks (SDNs) with digital televisions (TVs) into home environments, has the potential to provide smart TV services to users, and improve their quality of experience (QoE). In this regard, this paper focuses on one of the next generation services so-called follow me service (FMS). FMS is a service offered by 5gNB to user equipments (UEs) in indoor environments (e.g., home), it enables its clients to use their smart phones to select media content from content servers, then cast it on the nearest TV set (e.g., living room) and continue watching on the next TV set (e.g., kitchen) while moving around the indoor coverage area. FMS can be provisioned by utilizing UEs geoloca-tion information and robust mechanisms for switching between multiple 5G radio access technologies (RATs), based on the intelligence of the SDN/NFV intelligent home IP gateway of the Internet of Radio Light (IoRL) project paradigm. In view that the actual IoRL system is at its early development stage, we step forward by using Mininet platform to integrate SDN/NFV virtualization into 5G multi-RAT scenario and provide performance monitoring with measurements for the identified service. Simulation results show the effectiveness of our proposal under various use case scenarios by means of minimizing the packet loss rate and improving QoE of the home users. Index Terms-Software defined networks, network function virtualisation, quality of experience, Internet of radio light, intelligent home IP gateway

A quality of experience approach in smartphone video selection framework for energy efficiency

Online video streaming is getting more common in the smartphone device nowadays. Since the Corona Virus (COVID-19) pandemic hit all human across the globe in 2020, the usage of online streaming among smartphone user are getting more vital. Nevertheless, video streaming can cause the smartphone energy to drain quickly without user to realize it. Also, saving energy alone is not the most significant issues especially if with the lack of attention on the user Quality of Experience (QoE). A smartphones energy management is crucial to overcome both of these issues. Thus, a QoE Mobile Video Selection (QMVS) framework is proposed. The QMVS framework will govern the tradeoff between energy efficiency and user QoE in the smartphone device. In QMVS, video streaming will be using Dynamic Video Attribute Pre-Scheduling (DVAP) algorithm to determine the energy efficiency in smartphone devices. This process manages the video attribute such as brightness, resolution, and frame rate by turning to Video Content Selection (VCS). DVAP is handling a set of rule in the Rule Post-Pruning (RPP) method to remove an unused node in list tree of VCS. Next, QoE subjective method is used to obtain the Mean Opinion Score (MOS) of users from a survey experiment on QoE. After both experiment results (MOS and energy) are established, the linear regression technique is used to find the relationship between energy consumption and user QoE (MOS). The last process is to analyze the relationship of VCS results by comparing the DVAP to other recent video streaming applications available. Summary of experimental results demonstrate the significant reduction of 10% to 20% energy consumption along with considerable acceptance of user QoE. The VCS outcomes are essential to help users and developer deciding which suitable video streaming format that can satisfy energy consumption and user QoE

Resource Management in Multi-Access Edge Computing (MEC)

This PhD thesis investigates the effective ways of managing the resources of a Multi-Access Edge Computing Platform (MEC) in 5th Generation Mobile Communication (5G) networks. The main characteristics of MEC include distributed nature, proximity to users, and high availability. Based on these key features, solutions have been proposed for effective resource management. In this research, two aspects of resource management in MEC have been addressed. They are the computational resource and the caching resource which corresponds to the services provided by the MEC. MEC is a new 5G enabling technology proposed to reduce latency by bringing cloud computing capability closer to end-user Internet of Things (IoT) and mobile devices. MEC would support latency-critical user applications such as driverless cars and e-health. These applications will depend on resources and services provided by the MEC. However, MEC has limited computational and storage resources compared to the cloud. Therefore, it is important to ensure a reliable MEC network communication during resource provisioning by eradicating the chances of deadlock. Deadlock may occur due to a huge number of devices contending for a limited amount of resources if adequate measures are not put in place. It is crucial to eradicate deadlock while scheduling and provisioning resources on MEC to achieve a highly reliable and readily available system to support latency-critical applications. In this research, a deadlock avoidance resource provisioning algorithm has been proposed for industrial IoT devices using MEC platforms to ensure higher reliability of network interactions. The proposed scheme incorporates Banker’s resource-request algorithm using Software Defined Networking (SDN) to reduce communication overhead. Simulation and experimental results have shown that system deadlock can be prevented by applying the proposed algorithm which ultimately leads to a more reliable network interaction between mobile stations and MEC platforms. Additionally, this research explores the use of MEC as a caching platform as it is proclaimed as a key technology for reducing service processing delays in 5G networks. Caching on MEC decreases service latency and improve data content access by allowing direct content delivery through the edge without fetching data from the remote server. Caching on MEC is also deemed as an effective approach that guarantees more reachability due to proximity to endusers. In this regard, a novel hybrid content caching algorithm has been proposed for MEC platforms to increase their caching efficiency. The proposed algorithm is a unification of a modified Belady’s algorithm and a distributed cooperative caching algorithm to improve data access while reducing latency. A polynomial fit algorithm with Lagrange interpolation is employed to predict future request references for Belady’s algorithm. Experimental results show that the proposed algorithm obtains 4% more cache hits due to its selective caching approach when compared with case study algorithms. Results also show that the use of a cooperative algorithm can improve the total cache hits up to 80%. Furthermore, this thesis has also explored another predictive caching scheme to further improve caching efficiency. The motivation was to investigate another predictive caching approach as an improvement to the formal. A Predictive Collaborative Replacement (PCR) caching framework has been proposed as a result which consists of three schemes. Each of the schemes addresses a particular problem. The proactive predictive scheme has been proposed to address the problem of continuous change in cache popularity trends. The collaborative scheme addresses the problem of cache redundancy in the collaborative space. Finally, the replacement scheme is a solution to evict cold cache blocks and increase hit ratio. Simulation experiment has shown that the replacement scheme achieves 3% more cache hits than existing replacement algorithms such as Least Recently Used, Multi Queue and Frequency-based replacement. PCR algorithm has been tested using a real dataset (MovieLens20M dataset) and compared with an existing contemporary predictive algorithm. Results show that PCR performs better with a 25% increase in hit ratio and a 10% CPU utilization overhead

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