11,516 research outputs found

    Delivering Live Multimedia Streams to Mobile Hosts in a Wireless Internet with Multiple Content Aggregators

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    We consider the distribution of channels of live multimedia content (e.g., radio or TV broadcasts) via multiple content aggregators. In our work, an aggregator receives channels from content sources and redistributes them to a potentially large number of mobile hosts. Each aggregator can offer a channel in various configurations to cater for different wireless links, mobile hosts, and user preferences. As a result, a mobile host can generally choose from different configurations of the same channel offered by multiple alternative aggregators, which may be available through different interfaces (e.g., in a hotspot). A mobile host may need to handoff to another aggregator once it receives a channel. To prevent service disruption, a mobile host may for instance need to handoff to another aggregator when it leaves the subnets that make up its current aggregatorïżœs service area (e.g., a hotspot or a cellular network).\ud In this paper, we present the design of a system that enables (multi-homed) mobile hosts to seamlessly handoff from one aggregator to another so that they can continue to receive a channel wherever they go. We concentrate on handoffs between aggregators as a result of a mobile host crossing a subnet boundary. As part of the system, we discuss a lightweight application-level protocol that enables mobile hosts to select the aggregator that provides the ïżœbestïżœ configuration of a channel. The protocol comes into play when a mobile host begins to receive a channel and when it crosses a subnet boundary while receiving the channel. We show how our protocol can be implemented using the standard IETF session control and description protocols SIP and SDP. The implementation combines SIP and SDPïżœs offer-answer model in a novel way

    Modeling and Evaluation of Multisource Streaming Strategies in P2P VoD Systems

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    In recent years, multimedia content distribution has largely been moved to the Internet, inducing broadcasters, operators and service providers to upgrade with large expenses their infrastructures. In this context, streaming solutions that rely on user devices such as set-top boxes (STBs) to offload dedicated streaming servers are particularly appropriate. In these systems, contents are usually replicated and scattered over the network established by STBs placed at users' home, and the video-on-demand (VoD) service is provisioned through streaming sessions established among neighboring STBs following a Peer-to-Peer fashion. Up to now the majority of research works have focused on the design and optimization of content replicas mechanisms to minimize server costs. The optimization of replicas mechanisms has been typically performed either considering very crude system performance indicators or analyzing asymptotic behavior. In this work, instead, we propose an analytical model that complements previous works providing fairly accurate predictions of system performance (i.e., blocking probability). Our model turns out to be a highly scalable, flexible, and extensible tool that may be helpful both for designers and developers to efficiently predict the effect of system design choices in large scale STB-VoD system

    Random Linear Network Coding for 5G Mobile Video Delivery

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    An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

    Cloud Storage Performance and Security Analysis with Hadoop and GridFTP

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    Even though cloud server has been around for a few years, most of the web hosts today have not converted to cloud yet. If the purpose of the cloud server is distributing and storing files on the internet, FTP servers were much earlier than the cloud. FTP server is sufficient to distribute content on the internet. Therefore, is it worth to shift from FTP server to cloud server? The cloud storage provider declares high durability and availability for their users, and the ability to scale up for more storage space easily could save users tons of money. However, does it provide higher performance and better security features? Hadoop is a very popular platform for cloud computing. It is free software under Apache License. It is written in Java and supports large data processing in a distributed environment. Characteristics of Hadoop include partitioning of data, computing across thousands of hosts, and executing application computations in parallel. Hadoop Distributed File System allows rapid data transfer up to thousands of terabytes, and is capable of operating even in the case of node failure. GridFTP supports high-speed data transfer for wide-area networks. It is based on the FTP and features multiple data channels for parallel transfers. This report describes the technology behind HDFS and enhancement to the Hadoop security features with Kerberos. Based on data transfer performance and security features of HDFS and GridFTP server, we can decide if we should replace GridFTP server with HDFS. According to our experiment result, we conclude that GridFTP server provides better throughput than HDFS, and Kerberos has minimal impact to HDFS performance. We proposed a solution which users authenticate with HDFS first, and get the file from HDFS server to the client using GridFTP
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