Flexi-WVSNP-DASH: A Wireless Video Sensor Network Platform for the Internet of Things

abstract: Video capture, storage, and distribution in wireless video sensor networks (WVSNs) critically depends on the resources of the nodes forming the sensor networks. In the era of big data, Internet of Things (IoT), and distributed demand and solutions, there is a need for multi-dimensional data to be part of the Sensor Network data that is easily accessible and consumable by humanity as well as machinery. Images and video are expected to become as ubiquitous as is the scalar data in traditional sensor networks. The inception of video-streaming over the Internet, heralded a relentless research for effective ways of distributing video in a scalable and cost effective way. There has been novel implementation attempts across several network layers. Due to the inherent complications of backward compatibility and need for standardization across network layers, there has been a refocused attention to address most of the video distribution over the application layer. As a result, a few video streaming solutions over the Hypertext Transfer Protocol (HTTP) have been proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These frameworks, do not address the typical and future WVSN use cases. A highly flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH) are introduced. The platform's goal is to usher video as a data element that can be integrated into traditional and non-Internet networks. A low cost, scalable node is built from the ground up to be fully compatible with the Internet of Things Machine to Machine (M2M) concept, as well as the ability to be easily re-targeted to new applications in a short time. Flexi-WVSNP design includes a multi-radio node, a middle-ware for sensor operation and communication, a cross platform client facing data retriever/player framework, scalable security as well as a cohesive but decoupled hardware and software design.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Cognitive Video Streaming

Video-on-demand (VoD) streaming services are becoming increasingly popular due to their flexibility to allow users to access their favorite video contents anytime, anywhere from a wide range of access devices such as smart phones, computers and TV. The content providers rely on highly satisfied subscribers for revenue generation and there has been significant efforts in developing approaches to “estimate” the quality of experience (QoE) of VoD subscribers. But a key issue is that QoE is not defined, appropriate proxies needs to be found for QoE, via the streaming metrics (the quality of service (QoS) metrics) that are largely based on initial startup time, buffering delays, average bit rate and average throughput and other relevant factors such as the video content and user behavior and other external factors. The ultimate objective of the content provider is to elevate the QoE of all the subscribers at the cost of minimal network resources, such as hardware resources and bandwidth. We propose a cognitive video streaming strategy in order to ensure the QoE of subscribers while utilizing minimal network resources. The proposed cognitive video streaming architecture consists of an estimation module, a prediction module and an adaptation module. Then, we demonstrate the prediction module of the cognitive video streaming architecture through a play time prediction tool. For this purpose, the applicability of different machine learning algorithms such as k-nearest neighbor, neural network regression and survival models are experimented with; then, we develop an approach to identify the most relevant factors that contributed to the prediction. The proposed approaches are tested on data set provided by Comcast Cable

Closest playback-point first: A new peer selection algorithm for P2P VoD systems

Peer-to-peer (P2P) based video-on-demand (VoD) streaming service has been gaining popularity recently. Unlike live streaming, a VoD peer always starts its playback from the beginning of a stored video. The playback-points of different peers, as well as the amount of video contents/pieces they cached, depend on when they join the video session, or their viewing ages. As a result, the upload bandwidth of younger peers tends to be underutilized because older peers are not interested in their cached video pieces. The collaborative piece exchange among peers is undermined due to the unbalanced supply and demand. To address this issue, a playback-point based request peer selection algorithm is proposed in this paper. Specifically, when a peer requests a particular video piece, among the set of potential providers, a request is sent to the peer that has the smallest playback-point difference with itself. We call this request peer selection algorithm closest playback-point first (CPF). With CPF, peers with similar available content can be loosely grouped together for a more balanced collaborative piece exchange. Extensive packet-level simulations show that with CPF, the video playback quality is enhanced and the VoD server load is significantly reduced. © 2011 IEEE.published_or_final_versionThe IEEE Global Telecommunications Conference (GLOBECOM 2011), Houston, TX, USA, 5-9 December 201

A Survey on Adaptive Multimedia Streaming

Internet was primarily designed for one to one applications like electronic mail, reliable file transfer etc. However, the technological growth in both hardware and software industry have written in unprecedented success story of the growth of Internet and have paved the paths of modern digital evolution. In today’s world, the internet has become the way of life and has penetrated in its every domain. It is nearly impossible to list the applications which make use of internet in this era however, all these applications are data intensive and data may be textual, audio or visual requiring improved techniques to deal with these. Multimedia applications are one of them and have witnessed unprecedented growth in last few years. A predominance of that is by virtue of different video streaming applications in daily life like games, education, entertainment, security etc. Due to the huge demand of multimedia applications, heterogeneity of demands and limited resource availability there is a dire need of adaptive multimedia streaming. This chapter provides the detail discussion over different adaptive multimedia streaming mechanism over peer to peer network

Quality of experience for adaptive streaming using HTTP Dynamic Streaming

This thesis investigates how to establish the relationship between OSI layer 7 parameters of video streaming and the QoE of the user, and to evaluate which methods are most tting for the estimation of QoE. The project is made in cooperation with LTH and Acreo, and is a part of the Next generation over-the-top multimedia services (NOTTS) 7and the Eco system for Future Media Distribution (EFRAIM) project. The underlying techniques, which form the environment of our research of estimating the QoE, is adaptive bitrate streaming over TCP. The purpose is to investigate how a service, that provides a user with the means to benchmark the received quality of the Over the top (OTT) streaming service, can be built and distributed. Today there exists no such service that takes the viewers subjective opinion into consideration. There have been extensive research on some connected elds and issues but none with a unied solution to streaming adaptive bitrate video over TCP with its particular behavior and eect caused on the streamed video. In this report we evaluated two dierent methods of prediction of QoE, Pause Intensity based on the number of pauses and their length during playback, and a Linear bitrate model based on the average bitrate quality and its standard deviation. We also made a small user test with our streaming client software to evaluate the two methods to decide which one is the most benecial to use. The test showed that although one of the most irritating playback deficiencies is when pauses occur, the linear bitrate model delivered the most accurate predictions

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