Fair-RTT-DAS: A robust and efficient dynamic adaptive streaming over ICN

To sustain the adequate bandwidth demands over rapidly growing multimedia traffic and considering the effectiveness of Information-Centric Networking (ICN), recently, HTTP based Dynamic Adaptive Streaming (DASH) has been introduced over ICN, which significantly increases the network bandwidth utilisation. However, we identified that the inherent features of ICN also causes new vulnerabilities in the network. In this paper, we first propose a novel attack called as Bitrate Oscillation Attack (BOA), which exploits fundamental ICN characteristics: in-network caching and interest aggregation, to disrupt DASH functionality. In particular, the proposed attack forces the bitrate and resolution of video received by the attacked client to oscillate with high frequency and high amplitude during the streaming process. To detect and mitigate BOA, we design and implement a reactive countermeasure called Fair-RTT-DAS. Our solution ensures efficient bandwidth utilisation and improves the user perceived Quality of Experience (QoE) in the presence of varying content source locations. For this purpose, Fair-RTT-DAS consider DASH\u2019s two significant features: round-trip-time (RTT) and throughput fairness. In the presence of BOA in a network, our simulation results show an increase in the annoyance factor in user\u2019s spatial dimension, i.e., increase in oscillation frequency and amplitude. The results also show that our countermeasure significantly alleviates these adverse effects and makes dynamic adaptive streaming friendly to ICN\u2019s implicit features

Measurement And Improvement of Quality-of-Experience For Online Video Streaming Services

Title from PDF of title page, viewed on September 4, 2015Dissertation advisor: Deep MedhiVitaIncludes bibliographic references (pages 126-141)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2015HTTP based online video streaming services have been consistently dominating the online traffic for the past few years. Measuring and improving the performance of these services is an important challenge. Traditional Quality-of-Service (QoS) metrics such as packet loss, jitter and delay which were used for networked services are not easily understood by the users. Instead, Quality-of-Experience (QoE) metrics which capture the overall satisfaction are more suitable for measuring the quality as perceived by the users. However, these QoE metrics have not yet been standardized and their measurement and improvement poses unique challenges. In this work we first present a comprehensive survey of the different set of QoE metrics and the measurement methodologies suitable for HTTP based online video streaming services. We then present our active QoE measurement tool Pytomo that measures the QoE of YouTube videos. A case study on the measurement of QoE of YouTube videos when accessed by residential users from three different Internet Service Providers (ISP) in a metropolitan area is discussed. This is the first work that has collected QoE data from actual residential users using active measurements for YouTube videos. Based on these measurements we were able to study and compare the QoE of YouTube videos across multiple ISPs. We also were able to correlate the QoE observed with the server clusters used for the different users. Based on this correlation we were able to identify the server clusters that were experiencing diminished QoE. DynamicAdaptive Streaming overHTTP (DASH) is an HTTP based video streaming that enables the video players to adapt the video quality based on the network conditions. We next present a rate adaptation algorithm that improves the QoE of DASH video streaming services that selects the most optimum video quality. With DASH the video server hosts multiple representation of the same video and each representation is divided into small segments of constant playback duration. The DASH player downloads the appropriate representation based on the network conditions, thus, adapting the video quality to match the conditions. Currently deployed Adaptive Bitrate (ABR) algorithms use throughput and buffer occupancy to predict segment fetch times. These algorithms assume that the segments are of equal size. However, due to the encoding schemes employed this assumption does not hold. In order to overcome these limitations, we propose a novel Segment Aware Rate Adaptation algorithm (SARA) that leverages the knowledge of the segment size variations to improve the prediction of segment fetch times. Using an emulated player in a geographically distributed virtual network setup, we compare the performance of SARA with existing ABR algorithms. We demonstrate that SARA helps to improve the QoE of the DASH video streaming with improved convergence time, better bitrate switching performance and better video quality. We also show that unlike the existing adaptation schemes, SARA provides a consistent QoE irrespective of the segment size distributions.Introduction -- Measurement of QoE for Online Video Streaming Services: A Literature Survey -- Pytomo: A Tool for measuring QoE of YouTube Videos -- Case Study: QoE across three Internet Service Providers in a Metropolitan Area -- Adaptive Bitrate Algorithms for DASH -- Segment Aware Rate Adaptation for DASH -- Performance Evaluation of SARA -- Conclusion and Future Research --Appendix A. Sample MPD Fil

Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots

A policy-based framework towards smooth adaptive playback for dynamic video streaming over HTTP

The growth of video streaming in the Internet in the last few years has been highly significant and promises to continue in the future. This fact is related to the growth of Internet users and especially with the diversification of the end-user devices that happens nowadays. Earlier video streaming solutions didn´t consider adequately the Quality of Experience from the user’s perspective. This weakness has been since overcame with the DASH video streaming. The main feature of this protocol is to provide different versions, in terms of quality, of the same content. This way, depending on the status of the network infrastructure between the video server and the user device, the DASH protocol automatically selects the more adequate content version. Thus, it provides to the user the best possible quality for the consumption of that content. The main issue with the DASH protocol is associated to the loop, between each client and video server, which controls the rate of the video stream. In fact, as the network congestion increases, the client requests to the server a video stream with a lower rate. Nevertheless, due to the network latency, the DASH protocol in a standalone way may not be able to stabilize the video stream rate at a level that can guarantee a satisfactory QoE to the end-users. Network programming is a very active and popular topic in the field of network infrastructures management. In this area, the Software Defined Networking paradigm is an approach where a network controller, with a relatively abstracted view of the physical network infrastructure, tries to perform a more efficient management of the data path. The current work studies the combination of the DASH protocol and the Software Defined Networking paradigm in order to achieve a more adequate sharing of the network resources that could benefit both the users’ QoE and network management.O streaming de vídeo na Internet é um fenómeno que tem vindo a crescer de forma significativa nos últimos anos e que promete continuar a crescer no futuro. Este facto está associado ao aumento do número de utilizadores na Internet e, sobretudo, à crescente diversificação de dispositivos que se verifica atualmente. As primeiras soluções utilizadas no streaming de vídeo não acomodavam adequadamente o ponto de vista do utilizador na avaliação da qualidade do vídeo, i.e., a Qualidade de Experiência (QoE) do utilizador. Esta debilidade foi suplantada com o protocolo de streaming de vídeo adaptativo DASH. A principal funcionalidade deste protocolo é fornecer diferente versões, em termos de qualidade, para o mesmo conteúdo. Desta forma, dependendo do estado da infraestrutura de rede entre o servidor de vídeo e o dispositivo do utilizador, o protocolo DASH seleciona automaticamente a versão do conteúdo mais adequada a essas condições. Tal permite fornecer ao utilizador a melhor qualidade possível para o consumo deste conteúdo. O principal problema com o protocolo DASH está associado com o ciclo, entre cada cliente e o servidor de vídeo, que controla o débito de cada fluxo de vídeo. De facto, à medida que a rede fica congestionada, o cliente irá começar a requerer ao servidor um fluxo de vídeo com um débito menor. Ainda assim, devido à latência da rede, o protocolo DASH pode não ser capaz por si só de estabilizar o débito do fluxo de vídeo num nível que consiga garantir uma QoE satisfatória para os utilizadores. A programação de redes é uma área muito popular e ativa na gestão de infraestruturas de redes. Nesta área, o paradigma de Software Defined Networking é uma abordagem onde um controlador da rede, com um ponto de vista relativamente abstrato da infraestrutura física da rede, tenta desempenhar uma gestão mais eficiente do encaminhamento de rede. Neste trabalho estuda-se a junção do protocolo DASH e do paradigma de Software Defined Networking, de forma a atingir uma partilha mais adequada dos recursos da rede. O objetivo é implementar uma solução que seja benéfica tanto para a qualidade de experiência dos utilizadores como para a gestão da rede