7 research outputs found
Entrega de conteúdos multimédia em over-the-top: caso de estudo das gravações automáticas
Doutoramento em Engenharia EletrotécnicaOver-The-Top (OTT) multimedia delivery is a very appealing approach for providing
ubiquitous,
exible, and globally accessible services capable of low-cost
and unrestrained device targeting. In spite of its appeal, the underlying delivery
architecture must be carefully planned and optimized to maintain a high Qualityof-
Experience (QoE) and rational resource usage, especially when migrating from
services running on managed networks with established quality guarantees. To address
the lack of holistic research works on OTT multimedia delivery systems, this
Thesis focuses on an end-to-end optimization challenge, considering a migration
use-case of a popular Catch-up TV service from managed IP Television (IPTV)
networks to OTT. A global study is conducted on the importance of Catch-up
TV and its impact in today's society, demonstrating the growing popularity of
this time-shift service, its relevance in the multimedia landscape, and tness as
an OTT migration use-case. Catch-up TV consumption logs are obtained from
a Pay-TV operator's live production IPTV service containing over 1 million subscribers
to characterize demand and extract insights from service utilization at a
scale and scope not yet addressed in the literature. This characterization is used
to build demand forecasting models relying on machine learning techniques to enable
static and dynamic optimization of OTT multimedia delivery solutions, which
are able to produce accurate bandwidth and storage requirements' forecasts, and
may be used to achieve considerable power and cost savings whilst maintaining a
high QoE. A novel caching algorithm, Most Popularly Used (MPU), is proposed,
implemented, and shown to outperform established caching algorithms in both
simulation and experimental scenarios. The need for accurate QoE measurements
in OTT scenarios supporting HTTP Adaptive Streaming (HAS) motivates the creation
of a new QoE model capable of taking into account the impact of key HAS
aspects. By addressing the complete content delivery pipeline in the envisioned
content-aware OTT Content Delivery Network (CDN), this Thesis demonstrates
that signi cant improvements are possible in next-generation multimedia delivery
solutions.A entrega de conteúdos multimédia em Over-The-Top (OTT) e uma proposta
atractiva para fornecer um serviço flexível e globalmente acessível, capaz de alcançar qualquer dispositivo, com uma promessa de baixos custos. Apesar das suas vantagens, e necessario um planeamento arquitectural detalhado e optimizado para manter níveis elevados de Qualidade de Experiência (QoE), em particular aquando da migração dos serviços suportados em redes geridas com garantias de qualidade pré-estabelecidas. Para colmatar a falta de trabalhos de investigação na área de sistemas de entrega de conteúdos multimédia em OTT, esta Tese foca-se na optimização destas soluções como um todo, partindo do caso de uso de migração de um serviço popular de Gravações Automáticas suportado em redes de Televisão sobre IP (IPTV) geridas, para um cenário de entrega em OTT. Um estudo global para aferir a importância das Gravações Automáticas revela a sua relevância no panorama de serviços multimédia e a sua adequação enquanto caso de uso de
migração para cenários OTT. São obtidos registos de consumos de um serviço
de produção de Gravações Automáticas, representando mais de 1 milhão de assinantes,
para caracterizar e extrair informação de consumos numa escala e âmbito
não contemplados ate a data na literatura. Esta caracterização e utilizada para
construir modelos de previsão de carga, tirando partido de sistemas de machine
learning, que permitem optimizações estáticas e dinâmicas dos sistemas de entrega
de conteúdos em OTT através de previsões das necessidades de largura de banda e
armazenamento, potenciando ganhos significativos em consumo energético e custos.
Um novo mecanismo de caching, Most Popularly Used (MPU), demonstra um
desempenho superior as soluções de referencia, quer em cenários de simulação quer
experimentais. A necessidade de medição exacta da QoE em streaming adaptativo
HTTP motiva a criaçao de um modelo capaz de endereçar aspectos específicos
destas tecnologias adaptativas. Ao endereçar a cadeia completa de entrega através
de uma arquitectura consciente dos seus conteúdos, esta Tese demonstra que são
possíveis melhorias de desempenho muito significativas nas redes de entregas de
conteúdos em OTT de próxima geração
Estimation of the QoE for video streaming services based on facial expressions and gaze direction
As the multimedia technologies evolve, the need to control their quality becomes even more important making the Quality of Experience (QoE) measurements a key priority. Machine Learning (ML) can support this task providing models to analyse the information extracted by the multimedia. It is possible to divide the ML models applications in the following categories:
1) QoE modelling: ML is used to define QoE models which provide an output (e.g., perceived QoE score) for any given input (e.g., QoE influence factor).
2) QoE monitoring in case of encrypted traffic: ML is used to analyze passive traffic monitored data to obtain insight into degradations perceived by end-users.
3) Big data analytics: ML is used for the extraction of meaningful and useful information from the collected data, which can further be converted to actionable knowledge and utilized in managing QoE.
The QoE estimation quality task can be carried out by using two approaches: the objective approach and subjective one. As the two names highlight, they are referred to the pieces of information that the model analyses. The objective approach analyses the objective features extracted by the network connection and by the used media. As objective parameters, the state-of-the-art shows different approaches that use also the features extracted by human behaviour. The subjective approach instead, comes as a result of the rating approach, where the participants were asked to rate the perceived quality using different scales. This approach had the problem of being a time-consuming approach and for this reason not all the users agree to compile the questionnaire. Thus the direct evolution of this approach is the ML model adoption. A model can substitute the questionnaire and evaluate the QoE, depending on the data that analyses. By modelling the human response to the perceived quality on multimedia, QoE researchers found that the parameters extracted from the users could be different, like Electroencephalogram (EEG), Electrocardiogram (ECG), waves of the brain. The main problem with these techniques is the hardware. In fact, the user must wear electrodes in case of ECG and EEG, and also if the obtained results from these methods are relevant, their usage in a real context could be not feasible. For this reason, my studies have been focused on the developing of a Machine Learning framework completely unobtrusively based on the Facial reactions
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