Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support.

A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends

Enhancing Video Streaming Quality of DASH over Cloud/Edge Integrated Networks

With the advancement of mobile technologies and the popularity of mobile devices, mobile video streaming applications/services have increased considerably in recent years. Dynamic Adaptive Streaming over HTTP (DASH) or MPEG-DASH is one of the most widely used video streaming techniques over the Internet. It adapts video sending bit rate according to available network resources, however, in case of low bandwidth, DASH performs poorly, which will cause video quality degradation and video stalling. Mobile Edge Computing (MEC) or Multi-access Edge Computing, in connection with the backend cloud has been used to reduce latency and overcome some of the video quality degradation problems for mobile video streaming services. However, an end user might be suffering from video quality drop downs when s/he moves out from the coverage of one node to another or when the mobile network condition goes down. To tackle the degradation problems and assure enhanced video streaming quality, a novel follow-me Edge Node Prefetching (ENP) scheme was proposed and developed in the project, by prefetching video segments in advance in the upcoming node used by the end-user. A test bed was set up consisting of a backend cloud (OpenStack), two edge nodes (LXD Containers) and one mobile device, the ENP algorithm was implemented on the cloud server and client sides. Experiments were carried out for the DASH streaming service based on Dash.js from the DASH Industry Forum. Preliminary results show that the ENP scheme can maintain higher video quality and less service migration time when moving from one mobile node to another, when compared to existing approaches, i.e. live migration in Follow-me-Edge and the C-up schemes. The proposed scheme could be useful in smart city applications or providing seamless mobile video streaming services in Cloud/Edge integrated networks.Ibrahim Mohammedamee

Video Streaming Quality of Experience (QoE): In-network Cache Prefetching and Moving QoE Models

Title from PDF of title page viewed June 21, 2021VitaIncludes bibliographical references (pages 97-106)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2021Dissertation advisor: Deep MedhiVideo streaming accounts for a significant amount of traffic on the Internet. Users expect a high quality of experience with online video streaming. Service and content providers desire to provide a satisfying experience for end users. Therefore, developing metrics to measure users satisfaction of such services is crucial. Quality of Experience (QoE) of a user for a video streaming service is important for content providers. For video streaming, the DASH (Dynamic Adaptive Streaming over HTTP) standard is one of the common approaches for streaming used by content providers in which a video is divided into segments of different bit rates for delivery. In this research, we studied two inter-related problems for DASH video streaming: 1) in-network caching techniques, prefetching, and 2) QoE measurement and monitoring. In the first research direction, we focus on content providers utilizing in-network caching and prefetching in order to reduce video delivery latency to provide users a higher quality of experience and reduce the traffic load on the core network. The issue with current prefetching methods is that they do not utilize available resources well; thus, the end users are not able to receive the best possible QoE. These approaches are either mostly naive or they are not compatible with the DASH protocol and they are too complex consuming too much time and compute resources. We propose a smart video cache prefetching scheme for segment bitrates. Our prefetching approach is based on throughput values in the cache that are forecasted using previous throughput values from clients. Since in a cache environment, multiple clients contend for video segments in the cache, we assess the cache performance and also con- sider the impact on QoE for each client during contention. When comparing our scheme with an existing scheme, results show that our smart prefetching increases the cache hitrate and reduces the number of unused prefetches for the cache, thereby improving QoE of the clients. In our second research direction, we focus on objective QoE, for which a number of QoE models has been proposed. The limitations of the current models are that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. We refer to such models as static QoE models. In many situations, such as live events, ensemble QoE during the session is important to understand, especially for multiple clients together, for network and content providers. For this need, we propose two QoE models to capture QoE periodically during video streaming by multiple clients simultaneously, which we refer to as Moving QoE (MQoE) models. Our first model, MQoE_RF takes into consideration the nonlinear effect due to the bitrate gain and sensitivity from the bitrate switching frequency. Our second model, MQoE_SD focuses on capturing the standard deviation in the bitrate switching magnitude among segments. Then, we study the effectiveness of both the models in a multi-user mobile client environment. We compared our models with an extension of the Model Predictive Control (MPC) QoE model (referred to as MQoE_MO). Our study shows the robustness of our MQoE models. The results show how the MQoE models is able to more accurately capture the overall QoE behavior than the static QoE model and its extension.Introduction -- Literature survey -- Smart Cache Prefetching -- Smart Cache Prefetching evaluation -- Moving QoE Models -- Moving QoE Models evaluation -- Conclusio

Quality of experience-centric management of adaptive video streaming services : status and challenges

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years

Adaptive Video Streaming Testbed Design for Performance Study and Assessment of QoE

[EN] Hypertext Transfer Protocol adaptive streaming switches between different video qualities, adapting to the network conditions, and avoids stalling streamed frames over high¿oscillation client's throughput improving the users' quality of experience (QoE). Quality of experience has become the most important parameter to lead the service providers to know about the end¿user feedback. Implementing Hypertext Transfer Protocol adaptive streaming applications to find out QoE in real¿life scenarios of vast networks becomes more challenging and complex task regarding to cost, agile, time, and decisions. In this paper, a virtualized network testbed to virtualize various machines to support implementing experiments of adaptive video streaming has been developed. Within the test study, the metrics which demonstrate performance of QoE are investigated, respectively, including initial delay (ie, startup delay at the beginning of playback a video), frequency switches (ie, number of times the quality is changed), accumulative video time (ie, number and length of stalls), CPU usage, and battery energy consumption. Furthermore, the relation between effective parameters of QoS on the aforementioned metrics for different segment length is investigated. Experimental results show that the proposed virtualized system is agile, easy to install and use, and costs less than real testbeds. Moreover, the subjective and objective performance studies of QoE evaluation in the system have proven that the segment lengths of 6 to 8 seconds were faired and more efficient than others according to the investigated parameters.Ministerio de Economia y Competitividad, Grant/Award Number: TIN2014-57991-C3-1-PAbdullah, MTA.; Lloret, J.; Ali, A.; García-García, L. (2018). Adaptive Video Streaming Testbed Design for Performance Study and Assessment of QoE. International Journal of Communication Systems. 1-15. https://doi.org/10.1002/dac.3551S11

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

Survey of Transportation of Adaptive Multimedia Streaming service in Internet

[DE] World Wide Web is the greatest boon towards the technological advancement of modern era. Using the benefits of Internet globally, anywhere and anytime, users can avail the benefits of accessing live and on demand video services. The streaming media systems such as YouTube, Netflix, and Apple Music are reining the multimedia world with frequent popularity among users. A key concern of quality perceived for video streaming applications over Internet is the Quality of Experience (QoE) that users go through. Due to changing network conditions, bit rate and initial delay and the multimedia file freezes or provide poor video quality to the end users, researchers across industry and academia are explored HTTP Adaptive Streaming (HAS), which split the video content into multiple segments and offer the clients at varying qualities. The video player at the client side plays a vital role in buffer management and choosing the appropriate bit rate for each such segment of video to be transmitted. A higher bit rate transmitted video pauses in between whereas, a lower bit rate video lacks in quality, requiring a tradeoff between them. The need of the hour was to adaptively varying the bit rate and video quality to match the transmission media conditions. Further, The main aim of this paper is to give an overview on the state of the art HAS techniques across multimedia and networking domains. A detailed survey was conducted to analyze challenges and solutions in adaptive streaming algorithms, QoE, network protocols, buffering and etc. It also focuses on various challenges on QoE influence factors in a fluctuating network condition, which are often ignored in present HAS methodologies. Furthermore, this survey will enable network and multimedia researchers a fair amount of understanding about the latest happenings of adaptive streaming and the necessary improvements that can be incorporated in future developments.Abdullah, MTA.; Lloret, J.; Canovas Solbes, A.; García-García, L. (2017). Survey of Transportation of Adaptive Multimedia Streaming service in Internet. Network Protocols and Algorithms. 9(1-2):85-125. doi:10.5296/npa.v9i1-2.12412S8512591-