A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

Performance Evaluation And Anomaly detection in Mobile BroadBand Across Europe

With the rapidly growing market for smartphones and user’s confidence for immediate access to high-quality multimedia content, the delivery of video over wireless networks has become a big challenge. It makes it challenging to accommodate end-users with flawless quality of service. The growth of the smartphone market goes hand in hand with the development of the Internet, in which current transport protocols are being re-evaluated to deal with traffic growth. QUIC and WebRTC are new and evolving standards. The latter is a unique and evolving standard explicitly developed to meet this demand and enable a high-quality experience for mobile users of real-time communication services. QUIC has been designed to reduce Web latency, integrate security features, and allow a highquality experience for mobile users. Thus, the need to evaluate the performance of these rising protocols in a non-systematic environment is essential to understand the behavior of the network and provide the end user with a better multimedia delivery service. Since most of the work in the research community is conducted in a controlled environment, we leverage the MONROE platform to investigate the performance of QUIC and WebRTC in real cellular networks using static and mobile nodes. During this Thesis, we conduct measurements ofWebRTC and QUIC while making their data-sets public to the interested experimenter. Building such data-sets is very welcomed with the research community, opening doors to applying data science to network data-sets. The development part of the experiments involves building Docker containers that act as QUIC and WebRTC clients. These containers are publicly available to be used candidly or within the MONROE platform. These key contributions span from Chapter 4 to Chapter 5 presented in Part II of the Thesis. We exploit data collection from MONROE to apply data science over network data-sets, which will help identify networking problems shifting the Thesis focus from performance evaluation to a data science problem. Indeed, the second part of the Thesis focuses on interpretable data science. Identifying network problems leveraging Machine Learning (ML) has gained much visibility in the past few years, resulting in dramatically improved cellular network services. However, critical tasks like troubleshooting cellular networks are still performed manually by experts who monitor the network around the clock. In this context, this Thesis contributes by proposing the use of simple interpretable ML algorithms, moving away from the current trend of high-accuracy ML algorithms (e.g., deep learning) that do not allow interpretation (and hence understanding) of their outcome. We prefer having lower accuracy since we consider it interesting (anomalous) the scenarios misclassified by the ML algorithms, and we do not want to miss them by overfitting. To this aim, we present CIAN (from Causality Inference of Anomalies in Networks), a practical and interpretable ML methodology, which we implement in the form of a software tool named TTrees (from Troubleshooting Trees) and compare it to a supervised counterpart, named STress (from Supervised Trees). Both methodologies require small volumes of data and are quick at training. Our experiments using real data from operational commercial mobile networks e.g., sampled with MONROE probes, show that STrees and CIAN can automatically identify and accurately classify network anomalies—e.g., cases for which a low network performance is not justified by operational conditions—training with just a few hundreds of data samples, hence enabling precise troubleshooting actions. Most importantly, our experiments show that a fully automated unsupervised approach is viable and efficient. In Part III of the Thesis which includes Chapter 6 and 7. In conclusion, in this Thesis, we go through a data-driven networking roller coaster, from performance evaluating upcoming network protocols in real mobile networks to building methodologies that help identify and classify the root cause of networking problems, emphasizing the fact that these methodologies are easy to implement and can be deployed in production environments.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Matteo Sereno.- Secretario: Antonio de la Oliva Delgado.- Vocal: Raquel Barco Moren

Computational Resource Abuse in Web Applications

Internet browsers include Application Programming Interfaces (APIs) to support Web applications that require complex functionality, e.g., to let end users watch videos, make phone calls, and play video games. Meanwhile, many Web applications employ the browser APIs to rely on the user's hardware to execute intensive computation, access the Graphics Processing Unit (GPU), use persistent storage, and establish network connections. However, providing access to the system's computational resources, i.e., processing, storage, and networking, through the browser creates an opportunity for attackers to abuse resources. Principally, the problem occurs when an attacker compromises a Web site and includes malicious code to abuse its visitor's computational resources. For example, an attacker can abuse the user's system networking capabilities to perform a Denial of Service (DoS) attack against third parties. What is more, computational resource abuse has not received widespread attention from the Web security community because most of the current specifications are focused on content and session properties such as isolation, confidentiality, and integrity. Our primary goal is to study computational resource abuse and to advance the state of the art by providing a general attacker model, multiple case studies, a thorough analysis of available security mechanisms, and a new detection mechanism. To this end, we implemented and evaluated three scenarios where attackers use multiple browser APIs to abuse networking, local storage, and computation. Further, depending on the scenario, an attacker can use browsers to perform Denial of Service against third-party Web sites, create a network of browsers to store and distribute arbitrary data, or use browsers to establish anonymous connections similarly to The Onion Router (Tor). Our analysis also includes a real-life resource abuse case found in the wild, i.e., CryptoJacking, where thousands of Web sites forced their visitors to perform crypto-currency mining without their consent. In the general case, attacks presented in this thesis share the attacker model and two key characteristics: 1) the browser's end user remains oblivious to the attack, and 2) an attacker has to invest little resources in comparison to the resources he obtains. In addition to the attack's analysis, we present how existing, and upcoming, security enforcement mechanisms from Web security can hinder an attacker and their drawbacks. Moreover, we propose a novel detection approach based on browser API usage patterns. Finally, we evaluate the accuracy of our detection model, after training it with the real-life crypto-mining scenario, through a large scale analysis of the most popular Web sites

Investigating a theoretical framework for e-learning technology acceptance

E-learning has gained recognition and fame in delivering and distributing educational resources, and the same has become possible with the occurrence of Internet and Web technologies. The research seeks to determine the factors that influence students' acceptance of E-learning and to find out the way these factors determine the students' intention to employ E-learning. A theoretical framework was developed based on the technology acceptance model (TAM). To obtain information from the 270 university students who utilized the E-learning system, a questionnaire was formulated. The results revealed that “social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use” are the strongest and most important predictors in the intention of and students towards E-learning systems. The outcomes offer practical implications for practitioners, lawmakers, and developers in effective E-learning systems implementation to improve ongoing interests and activities of university students in a virtual E-learning atmosphere, valuable recommendations for E-learning practices are given by the research findings, and these may turn out to be as guidelines for the efficient design of E-learning systems

Abordando la medición automática de la experiencia de la audiencia en línea

Trabajo de Fin de Grado del Doble Grado en Ingeniería Informática y Matemáticas, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2020/2021The availability of automatic and personalized feedback is a large advantage when facing an audience. An effective way to give such feedback is to analyze the audience experience, which provides valuable information about the quality of a speech or performance. In this document, we present the design and implementation of a computer vision system to automatically measure audience experience. This includes the definition of a theoretical and practical framework grounded on the theatrical perspective to quantify this concept, the development of an artificial intelligence system which serves as a proof-of-concept of our approach, and the creation of a dataset to train our system. To facilitate the data collection step, we have also created a custom video conferencing tool. Additionally, we present the evaluation of our artificial intelligence system and the final conclusions.La disponibilidad de feedback automático y personalizado supone una gran ventaja a la hora de enfrentarse a un público. Una forma efectiva de dar este tipo de feedback es analizar la experiencia de la audiencia, que proporciona información fundamental sobre la calidad de una ponencia o actuación. En este documento exponemos el diseño e implementación de un sistema automático de medición de la experiencia de la audiencia basado en la visión por computador. Esto incluye la definición de un marco teórico y práctico fundamentado en la perspectiva del mundo del teatro para cuantificar el concepto de experiencia de la audiencia, el desarrollo de un sistema basado en inteligencia artificial que sirve como prototipo de nuestra aproximación y la recopilación un conjunto de datos para entrenar el sistema. Para facilitar este último paso hemos desarrolado una aplicación de videoconferencias personalizada. Además, en este trabajo presentamos la evaluación de nuestro sistema de inteligencia artificial y las conclusiones extraídas.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Video-assisted Overtaking System enabled by V2V Communications

V2X (Vehicle-to-Everything) is a promising technology to diminish road hazards and increase driving safety. This thesis focuses in the transmission of video between vehicles (V2V, Vehicle-to-Vehicle) in an overtaking situation, helping drivers to be more aware and less error-prone in these situations. In the implementation, the vehicle reads from vehicle's CAN and GPS data to setup the system, streams his Line of Sight to the overtaking vehicle and uses DSRC as the communication technology

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