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

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

The Effective Transmission and Processing of Mobile Multimedia

Ph.DDOCTOR OF PHILOSOPH

QoE de streaming de vídeo em redes veiculares com multihoming

With the ever-increasing interest and availability of vehicular networks, it is important to study the Quality-of-Experience provided by these networks, which ultimately determines the general public perception and thus the overall user adoption. The broad Internet access, the evolution of user equipment, such as smartphones, tablets and personal computers, and the appearance of services like Youtube and Netflix, is leading the user content consumption to be more and more in the form of video streaming. Either motivated by safety or commercial applications, video streaming in such highly mobile environments offers multiple challenges. This dissertation evaluates the QoE of a multihoming communication strategy, supported simultaneously byWAVE and Wi-Fi, for increasing the reliability and performance of video streams in these environments. Furthermore, it also investigates how distinct network functionalities, such as multihoming load balance, buffering, and network metrics such as throughput and latency affect the overall QoE observed. The results obtained led to the proposal of a multihoming load balance policy for video applications based on access technologies, aiming to improve QoE. The overall results show that QoE improves by 7.5% using the proposed approach.Com o aumento contínuo do interesse e disponibilidade de redes veiculares, é importante agora estudar a Qualidade de Experiência fornecida por estas redes, que fundamentalmente determina a opinião e a percepção do público geral sobre um dado serviço. O vasto acesso à Internet, a evolução dos equipamentos, como os telemóveis atuais, tablets e computadores pessoais, e o aparecimento de serviços como o YouTube e o Netflix, está a fazer com que o conteúdo mais consumido seja cada vez mais em forma de streaming de vídeo. Quer seja motivado por aplicações de segurança ou comerciais, o streaming de vídeo em ambientes altamente móveis levanta vários desafios. Esta dissertação avalia a Qualidade de Experiência de técnicas de multihoming, permitindo o uso de diferentes tecnologias de comunicação, como o WAVE e o Wi-Fi, para aumentar a fiabilidade e desempenho de streams de vídeo nestes ambientes. Para além disso, investiga também como é que diferentes mecanismos de rede, como o balanceamento, multihoming e o buffering, e métricas como a taxa de transferência e latência, afetam a QoE observada. Os resultados obtidos levaram à proposta de uma política de divisão de tráfego para aplicações de vídeo baseada em tecnologias de acesso para situações de multihoming, visando uma melhoria da QoE do utilizador. Utilizando o método proposto, os resultados mostram que a experiência do utilizador tem uma melhoria de 7,5%.Mestrado em Engenharia de Computadores e Telemátic

A Survey on Mobile Edge Computing for Video Streaming : Opportunities and Challenges

5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowd-sourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-to-end communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultra-reliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-of-the-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.publishedVersionPeer reviewe