Adaptive Streaming: A subjective catalog to assess the performance of objective QoE metrics

Scalable streaming has emerged as a feasible solution to resolve users' heterogeneity problems. SVC is the technology that has served as the definitive impulse for the growth of streaming adaptive systems. Systems seek to improve layer switching efficiency from the network point of view but, with increasing importance, without jeopardizing user perceived video quality, i.e., QoE. We have performed extensive subjective experiments to corroborate the preference towards adaptive systems when compared to traditional non-adaptive systems. The resulting subjective scores are correlated with most relevant Full Reference (FR) objective metrics. We obtain an exponential relationship between human decisions and the same decisions expressed as a difference of objective metrics. A strong correlation with subjective scores validates objective metrics to be used as aid in the adaptive decision taking algorithms to improve overall systems performance. Results show that, among the evaluated objective metrics, PSNR is the metric that provide worse results in terms of reproducing the human decision

Towards video streaming in IoT environments: vehicular communication perspective

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues

Adaptive Streaming in P2P Live Video Systems: A Distributed Rate Control Approach

Dynamic Adaptive Streaming over HTTP (DASH) is a recently proposed standard that offers different versions of the same media content to adapt the delivery process over the Internet to dynamic bandwidth fluctuations and different user device capabilities. The peer-to-peer (P2P) paradigm for video streaming allows to leverage the cooperation among peers, guaranteeing to serve every video request with increased scalability and reduced cost. We propose to combine these two approaches in a P2P-DASH architecture, exploiting the potentiality of both. The new platform is made of several swarms, and a different DASH representation is streamed within each of them; unlike client-server DASH architectures, where each client autonomously selects which version to download according to current network conditions and to its device resources, we put forth a new rate control strategy implemented at peer site to maintain a good viewing quality to the local user and to simultaneously guarantee the successful operation of the P2P swarms. The effectiveness of the solution is demonstrated through simulation and it indicates that the P2P-DASH platform is able to warrant its users a very good performance, much more satisfying than in a conventional P2P environment where DASH is not employed. Through a comparison with a reference DASH system modeled via the Integer Linear Programming (ILP) approach, the new system is shown to outperform such reference architecture. To further validate the proposal, both in terms of robustness and scalability, system behavior is investigated in the critical condition of a flash crowd, showing that the strong upsurge of new users can be successfully revealed and gradually accommodated.Comment: 12 pages, 17 figures, this work has been submitted to the IEEE journal on selected Area in Communication

QoE assessment for SVC streaming in ENVISION

Scalable video coding has drawn great interest in content delivery in many multimedia services thanks to its capability to handle terminal heterogeneity and network conditions variation. In our previous work, and under the umbrella of ENVISION, we have proposed a playout smoothing mechanism to ensure the uniform delivery of the layered stream, by reducing the quality changes that the stream undergoes when adapting to changing network conditions. In this paper we study the resulting video quality, from the final user perception under different network conditions of loss and delays. For that we have adopted the Double Stimulus Impairment Scale (DSIS) method. The results show that the Mean Opinion Score for the smoothed video clips was higher under different network configuration. This confirms the effectiveness of the proposed smoothing mechanism.Comment: IEEE 20th International Conference on Electronics, Circuits, and Systems (IEEE ICECS 2013), Abu Dhabi : United Arab Emirates (2013

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

A QoE based performance study of mobile peer-to-peer live video streaming

Peer-to-peer (P2P) Mobile Ad Hoc Networks (MANETs) are widely envisioned to be a practical platform to mobile live video streaming applications (e.g., mobile IPTV). However, the performance of such a streaming solution is still largely unknown. As such, in this paper, we aim to quantify the streaming performance using a Quality of Experience (QoE) based approach. Our simulation results indicate that video streaming performance is highly sensitive to the video chunk size. Specifically, if the chunk size is small, performance, in terms of both QoE and QoS, is guaranteed but at the expense of a higher overhead. On the other hand, if chunk size is increased, performance can degrade quite rapidly. Thus, it needs some careful fine tuning of chunk size to obtain satisfactory QoE performance. © 2012 IEEE.published_or_final_versio

A credit-based approach to scalable video transmission over a peer-to-peer social network

PhDThe objective of the research work presented in this thesis is to study scalable video transmission over peer-to-peer networks. In particular, we analyse how a credit-based approach and exploitation of social networking features can play a significant role in the design of such systems. Peer-to-peer systems are nowadays a valid alternative to the traditional client-server architecture for the distribution of multimedia content, as they transfer the workload from the service provider to the final user, with a subsequent reduction of management costs for the former. On the other hand, scalable video coding helps in dealing with network heterogeneity, since the content can be tailored to the characteristics or resources of the peers. First of all, we present a study that evaluates subjective video quality perceived by the final user under different transmission scenarios. We also propose a video chunk selection algorithm that maximises received video quality under different network conditions. Furthermore, challenges in building reliable peer-to-peer systems for multimedia streaming include optimisation of resource allocation and design mechanisms based on rewards and punishments that provide incentives for users to share their own resources. Our solution relies on a credit-based architecture, where peers do not interact with users that have proven to be malicious in the past. Finally, if peers are allowed to build a social network of trusted users, they can share the local information they have about the network and have a more complete understanding of the type of users they are interacting with. Therefore, in addition to a local credit, a social credit or social reputation is introduced. This thesis concludes with an overview of future developments of this research work

Distribuição de conteúdos over-the-top multimédia em redes sem fios

mestrado em Engenharia Eletrónica e TelecomunicaçõesHoje em dia a Internet é considerada um bem essencial devido ao facto de haver uma constante necessidade de comunicar, mas também de aceder e partilhar conteúdos. Com a crescente utilização da Internet, aliada ao aumento da largura de banda fornecida pelos operadores de telecomunicações, criaram-se assim excelentes condições para o aumento dos serviços multimédia Over-The-Top (OTT), demonstrado pelo o sucesso apresentado pelos os serviços Netflix e Youtube. O serviço OTT engloba a entrega de vídeo e áudio através da Internet sem um controlo direto dos operadores de telecomunicações, apresentando uma proposta atractiva de baixo custo e lucrativa. Embora a entrega OTT seja cativante, esta padece de algumas limitações. Para que a proposta se mantenha em crescimento e com elevados padrões de Qualidade-de-Experiência (QoE) para os consumidores, é necessário investir na arquitetura da rede de distribuição de conteúdos, para que esta seja capaz de se adaptar aos diversos tipos de conteúdo e obter um modelo otimizado com um uso cauteloso dos recursos, tendo como objectivo fornecer serviços OTT com uma boa qualidade para o utilizador, de uma forma eficiente e escalável indo de encontro aos requisitos impostos pelas redes móveis atuais e futuras. Esta dissertação foca-se na distribuição de conteúdos em redes sem fios, através de um modelo de cache distribuída entre os diferentes pontos de acesso, aumentando assim o tamanho da cache e diminuindo o tráfego necessário para os servidores ou caches da camada de agregação acima. Assim, permite-se uma maior escalabilidade e aumento da largura de banda disponível para os servidores de camada de agregação acima. Testou-se o modelo de cache distribuída em três cenários: o consumidor está em casa em que se considera que tem um acesso fixo, o consumidor tem um comportamento móvel entre vários pontos de acesso na rua, e o consumidor está dentro de um comboio em alta velocidade. Testaram-se várias soluções como Redis2, Cachelot e Memcached para servir de cache, bem como se avaliaram vários proxies para ir de encontro ás características necessárias. Mais ainda, na distribuição de conteúdos testaram-se dois algoritmos, nomeadamente o Consistent e o Rendezvouz Hashing. Ainda nesta dissertação utilizou-se uma proposta já existente baseada na previsão de conteúdos (prefetching ), que consiste em colocar o conteúdo nas caches antes de este ser requerido pelos consumidores. No final, verificou-se que o modelo distribuído com a integração com prefecthing melhorou a qualidade de experiência dos consumidores, bem como reduziu a carga nos servidores de camada de agregação acima.Nowadays, the Internet is considered an essential good, due to the fact that there is a need to communicate, but also to access and share information. With the increasing use of the Internet, allied with the increased bandwidth provided by telecommunication operators, it has created conditions for the increase of Over-the-Top (OTT) Multimedia Services, demonstrated by the huge success of Net ix and Youtube. The OTT service encompasses the delivery of video and audio through the Internet without direct control of telecommunication operators, presenting an attractive low-cost and pro table proposal. Although the OTT delivery is captivating, it has some limitations. In order to increase the number of clients and keep the high Quality of Experience (QoE) standards, an enhanced architecture for content distribution network is needed. Thus, the enhanced architecture needs to provide a good quality for the user, in an e cient and scalable way, supporting the requirements imposed by future mobile networks. This dissertation aims to approach the content distribution in wireless networks, through a distributed cache model among the several access points, thus increasing the cache size and decreasing the load on the upstream servers. The proposed architecture was tested in three di erent scenarios: the consumer is at home and it is considered that it has a xed access, the consumer is mobile between several access points in the street, the consumer is in a high speed train. Several solutions were evaluated, such as Redis2, Cachelot and Memcached to serve as caches, along with the evaluation of several proxies server in order to ful ll the required features. Also, it was tested two distributed algorithms, namely the Consistent and Rendezvous Hashing. Moreover, in this dissertation it was integrated a prefetching mechanism, which consists of inserting the content in caches before being requested by the consumers. At the end, it was veri ed that the distributed model with prefetching improved the consumers QoE as well as it reduced the load on the upstream servers