Due to its flexibility, scalability, real-time, and rich QoS features, Data Distribution Service (DDS) middleware provides seamless\nintegration with high-performance, real-time, and mission-critical networks. Unlike traditional client-server communication\nmodels, DDS is based on the publish/subscribe communication model. DDS improves video streaming quality through its efficient\nand high-performance data delivery mechanism. This paper studies and investigates how DDS is suitable for streaming real-time\nfull-motion video over a communication network. Experimental studies are conducted to compare video streaming using a the\nVLC player with the DDS overlay. Our results depict the superiority of DDS in provisioning quality video streams at the cost of low\nnetwork bandwidth.The results also showthatDDS is more scalable and flexible and is a promised technology for video distribution\nover IP networks where it uses much less bandwidth while maintaining high quality video stream delivery.\n1. Introduction\nVideo streaming applications are experiencing fast growth\nand demand for diverse business needs. Applications of video\nstreaming include, for example, commercial applications\nsuch as e-learning, video conferencing, stored-video streaming;\nand military applications such as video surveillance of\ntargeted field or specific objects. Video traffic is resource\nintensive and consumes a lot of network bandwidth; therefore\nit is challenging issue to streamvideo over limited-bandwidth\nnetworks, for example, WSN or Bluetooth. In many cases,\nbandwidth usage implies direct cost on end-users. In this\nwork, we try to enhance the end-user experience both in\nterms of quality and cost, through the deployment of theDDS\nmiddleware.\n1.1. DDS Overview and Video QoS Polices. DDS stands\nfor Data Distribution Service. It is a set of specifications\nstandardized by the Object Management Group (OMG).\nThe DDS middleware is a known standard with built-in\ndata-structures and attributes specified by meta-information\ncalled topics. Every topic describes a set of associated datasamples\nwith the same data-property and data-structure. For\nexample, a topic named ââ?¬Å?temperatureââ?¬Â can
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