Video streaming

B

Challenges of future multimedia QoE monitoring for internet service providers

The ever-increasing network traffic and user expectations at reduced cost make the delivery of high Quality of Experience (QoE) for multimedia services more vital than ever in the eyes of Internet Service Providers (ISPs). Real-time quality monitoring, with a focus on the user, has become essential as the first step in cost-effective provisioning of high quality services. With the recent changes in the perception of user privacy, the rising level of application-layer encryption and the introduction and deployment of virtualized networks, QoE monitoring solutions need to be adapted to the fast changing Internet landscape. In this contribution, we provide an overview of state-of-the-art quality monitoring models and probing technologies, and highlight the major challenges ISPs have to face when they want to ensure high service quality for their customers

Метод аналізу взаємозалежностей параметрів QoE та QoS на основі алгоритмів машинного навчання

Робота публікується згідно наказу Ректора НАУ від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії університету". Керівник дипломної роботи: професор кафедри ТКРС, Одарченко Р.С.Сьогодні більшість постачальників телекомунікаційних послуг зацікавлені в тому, щоб клієнти були задоволені послугами, які їм надаються. Очевидно, що користувачі певної послуги будуть і надалі обирати одного і того ж провайдера, якщо їх очікування якості послуг виправдалися. Тому, щоб задовольняти очікування клієнтів, провайдерам необхідно постійно вимірювати поточний рівень якості послуг, які вони надають. Проведення таких вимірювань називають процесом моніторингу якості. Розрізняють якість обслуговування (QoS) та якість моніторингу досвіду (QoE). Якість обслуговування (QoS) визначається як сукупність характеристик телекомунікаційної послуги, яка впливає на її здатність задовольняти заявлені та непрямі потреби користувача. Якість моніторингу досвіду (QoE) – це «ступінь задоволення або роздратування користувача від користування програмою чи послугою

Uma abordagem preditiva de DASH QoE baseada em aprendizado de máquina em multi-access edge computing

Orientador: Christian Rodolfo Esteve RothenbergDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O tráfego de serviços de vídeo multimídia está crescendo rapidamente nas redes móveis nos últimos anos. Os serviços de vídeo que usam técnicas de Dynamic Adaptive Streaming sobre HTTP (DASH) dominaram o tráfego total da Internet para transportar o tráfego de vídeo. Espera-se que as operadoras de rede móvel (Mobile Network Operators - MNOs) continuem atendendo a essa demanda crescente por tráfego de vídeo suportado por DASH, ao mesmo tempo em que fornecem uma alta qualidade de experiência (Quality of Experience - QoE) aos usuários finais. Além disso, as operadoras precisam ter um conhecimento claro acerca da qualidade de vídeo percebida pelos usuários finais e relacioná-la com o monitoramento em nível de rede, ou com informações de telemetria para identificação de problemas, análise da causa raiz e predição de padrões. Para garantir um gerenciamento de tráfego de rede com reconhecimento de QoE, um pré-requisito é que os MNOs monitorem o tráfego de rede passivamente e realizem medições efetivas de indicadores-chave de desempenho (Key Performance Indicators - KPIs) de QoE, como resoluções, eventos de paralisação, entre outros, que influenciam diretamente a percepção do usuário final. Muitas abordagens da literatura foram propostas para medir os KPIs com o objetivo de fornecer uma qualidade de serviço de vídeo aceitável. A maioria das soluções exige consciência de contexto do usuário final, o que não é viável do ponto de vista do MNO. No entanto, Deep Packet Inspection (DPI), outra solução mais amplamente usada para estimar os KPIs diretamente do tráfego de rede, não é mais uma solução conveniente para as operadoras devido à adoção de criptografia de streaming de vídeo fim-a-fim sobre TCP (HTTPs) e QUIC. Portanto, o aprendizado de máquina (Machine Learning - ML) passou a ser recentemente aceito como uma solução bem reconhecida para estimar KPIs de QoE, analisando os padrões de tráfego criptografados bem como estatísticas como qualidade de serviço (Quality of Service - QoS). Este trabalho apresenta uma abordagem mais refinada e leve, baseada em aprendizado de máquina, denominada Edge QoE Probe, para estimar QoE do usuário final para o serviço de vídeo DASH, monitorando passivamente o tráfego de rede criptografado na borda da rede. Nossa abordagem pode avaliar vários KPIs de QoE, como por exemplo resolução, taxa de bits, proporção de paralisação, entre outros, tanto em tempo real quanto por sessão. Além disso, neste trabalho investigamos o desempenho do vídeo DASH sobre o protocolo de transporte tradicional TCP (HTTPs) e QUIC. Para este propósito, avaliamos experimentalmente diferentes traces de rede celular em um ambiente emulado de alta fidelidade e comparamos o desempenho comportamental de algoritmos Adaptive Bitrate Streaming (ABS) considerando KPIs de QoE sobre TCP (HTTPs) e QUIC. Nossos resultados empíricos mostram que os algoritmos tradicionais de ABS usando QUIC como transporte precisariam alterações específicas para melhorar o desempenho em termos de QoE de vídeo baseados em DASHAbstract: Multimedia video services traffic is rapidly growing in mobile networks in recent years. Video services using Dynamic Adaptive Streaming over HTTP (DASH) techniques have dominated the total internet traffic to carry video traffic. Mobile Network Operators (MNOs) are expected to run on with this growing demand for DASH-supported video traffic while providing a high Quality of Experience (QoE) to the end-users. Besides, operators need to have a crystal notion of video quality perceived by the end-users and correlate them with network-level monitoring or telemetry information for problem identification, root cause analysis, and pattern prediction. To ensure QoE–aware network traffic management, a prerequisite for the MNOs is to monitor the network traffic passively and measure objective QoE Key Performance Indicators (KPIs) (such as resolutions and stalling events) effectively that directly influence end-user subjective feedback. Many literature approaches have been proposed to measure the KPIs aimed to deliver acceptable video service quality. Most of the solutions require end-user awareness, which is not viable from the MNOs' perspective. However, Deep Packet Inspection (DPI), another most widely used solution to estimate the KPIs directly from network traffic, is not a convenient solution anymore for the operators due to the adoption of end-to-end video streaming encryption over TCP (HTTPs) and QUIC transport protocol. Hence, in recent, Machine Learning (ML) has been accepted as a well-recognized solution for estimating QoE KPIs by analyzing the encrypted traffic patterns and statistics as Quality of Service (QoS). This work presents an ML-based lightweight and fine-grained Edge QoE Probe approach to estimate the end-user QoE for DASH video service by passively monitoring the encrypted network traffic on the edge of the network. Our approach can assess numerous QoE KPIs (such as resolution, bit-rate, quality switches, startup delay, and stall ratio) both in a real-time and per-session manner. Moreover, we investigate the DASH video service performance over the traditional TCP (HTTPs) and QUIC transport protocol in this work. For this purpose, we experimentally evaluate different cellular network traces in a high-fidelity emulated testbed and compare the behavioral performance of Adaptive Bitrate Streaming (ABS) algorithms considering QoE KPIs over TCP (HTTPs) and QUIC. Our empirical results show that QUIC suffers from traditional state-of-the-art ABS algorithms' ineffectiveness to improve video streaming performance without specific changesMestradoEngenharia de ComputaçãoMestre em Engenharia ElétricaFuncam

Contribution to quality of user experience provision over wireless networks

The widespread expansion of wireless networks has brought new attractive possibilities to end users. In addition to the mobility capabilities provided by unwired devices, it is worth remarking the easy configuration process that a user has to follow to gain connectivity through a wireless network. Furthermore, the increasing bandwidth provided by the IEEE 802.11 family has made possible accessing to high-demanding services such as multimedia communications. Multimedia traffic has unique characteristics that make it greatly vulnerable against network impairments, such as packet losses, delay, or jitter. Voice over IP (VoIP) communications, video-conference, video-streaming, etc., are examples of these high-demanding services that need to meet very strict requirements in order to be served with acceptable levels of quality. Accomplishing these tough requirements will become extremely important during the next years, taking into account that consumer video traffic will be the predominant traffic in the Internet during the next years. In wired systems, these requirements are achieved by using Quality of Service (QoS) techniques, such as Differentiated Services (DiffServ), traffic engineering, etc. However, employing these methodologies in wireless networks is not that simple as many other factors impact on the quality of the provided service, e.g., fading, interferences, etc. Focusing on the IEEE 802.11g standard, which is the most extended technology for Wireless Local Area Networks (WLANs), it defines two different architecture schemes. On one hand, the infrastructure mode consists of a central point, which manages the network, assuming network controlling tasks such as IP assignment, routing, accessing security, etc. The rest of the nodes composing the network act as hosts, i.e., they send and receive traffic through the central point. On the other hand, the IEEE 802.11 ad-hoc configuration mode is less extended than the infrastructure one. Under this scheme, there is not a central point in the network, but all the nodes composing the network assume both host and router roles, which permits the quick deployment of a network without a pre-existent infrastructure. This type of networks, so called Mobile Ad-hoc NETworks (MANETs), presents interesting characteristics for situations when the fast deployment of a communication system is needed, e.g., tactics networks, disaster events, or temporary networks. The benefits provided by MANETs are varied, including high mobility possibilities provided to the nodes, network coverage extension, or network reliability avoiding single points of failure. The dynamic nature of these networks makes the nodes to react to topology changes as fast as possible. Moreover, as aforementioned, the transmission of multimedia traffic entails real-time constraints, necessary to provide these services with acceptable levels of quality. For those reasons, efficient routing protocols are needed, capable of providing enough reliability to the network and with the minimum impact to the quality of the service flowing through the nodes. Regarding quality measurements, the current trend is estimating what the end user actually perceives when consuming the service. This paradigm is called Quality of user Experience (QoE) and differs from the traditional Quality of Service (QoS) approach in the human perspective given to quality estimations. In order to measure the subjective opinion that a user has about a given service, different approaches can be taken. The most accurate methodology is performing subjective tests in which a panel of human testers rates the quality of the service under evaluation. This approach returns a quality score, so-called Mean Opinion Score (MOS), for the considered service in a scale 1 - 5. This methodology presents several drawbacks such as its high expenses and the impossibility of performing tests at real time. For those reasons, several mathematical models have been presented in order to provide an estimation of the QoE (MOS) reached by different multimedia services In this thesis, the focus is on evaluating and understanding the multimedia-content transmission-process in wireless networks from a QoE perspective. To this end, firstly, the QoE paradigm is explored aiming at understanding how to evaluate the quality of a given multimedia service. Then, the influence of the impairments introduced by the wireless transmission channel on the multimedia communications is analyzed. Besides, the functioning of different WLAN schemes in order to test their suitability to support highly demanding traffic such as the multimedia transmission is evaluated. Finally, as the main contribution of this thesis, new mechanisms or strategies to improve the quality of multimedia services distributed over IEEE 802.11 networks are presented. Concretely, the distribution of multimedia services over ad-hoc networks is deeply studied. Thus, a novel opportunistic routing protocol, so-called JOKER (auto-adJustable Opportunistic acK/timEr-based Routing) is presented. This proposal permits better support to multimedia services while reducing the energy consumption in comparison with the standard ad-hoc routing protocols.Universidad Politécnica de CartagenaPrograma Oficial de Doctorado en Tecnologías de la Información y Comunicacione