Due to its flexibility, scalability, real-time, and rich QoS features, Data Distribution Service (DDS) middleware provides seamless integration with high-performance, real-time, and mission-critical networks. Unlike traditional client-server communication models, DDS is based on the publish/subscribe communication model. DDS improves video streaming quality through its efficient and high-performance data delivery mechanism. This paper studies and investigates how DDS is suitable for streaming real-time full-motion video over a communication network. Experimental studies are conducted to compare video streaming using a the VLC player with the DDS overlay. Our results depict the superiority of DDS in provisioning quality video streams at the cost of low network bandwidth. The results also show that DDS is more scalable and flexible and is a promised technology for video distribution over IP networks where it uses much less bandwidth while maintaining high quality video stream delivery. 1. Introduction Video streaming applications are experiencing fast growth and demand for diverse business needs. Applications of video streaming include, for example, commercial applications such as e-learning, video conferencing, stored-video streaming; and military applications such as video surveillance of targeted field or specific objects. Video traffic is resource intensive and consumes a lot of network bandwidth; therefore it is challenging issue to stream video over limited-bandwidth networks, for example, WSN or Bluetooth. In many cases, bandwidth usage implies direct cost on end-users. In this work, we try to enhance the end-user experience both in terms of quality and cost, through the deployment of the DDS middleware. 1.1. DDS Overview and Video QoS Polices DDS stands for Data Distribution Service. It is a set of specifications standardized by the Object Management Group (OMG). The DDS middleware is a known standard with built-in data-structures and attributes specified by meta-information called topics. Every topic describes a set of associated data-samples with the same data-property and data-structure. For example, a topic named “temperature” can be used to store samples of temperature monitored by a distributed set of sensors [1]. The entities that write and read the data-samples using a DDS-based middleware are the publishers and the subscribers. A publisher consists of a set of data writer modules, each of which is used to write information on a particular topic. On the other hand, a subscriber reads the data samples of topics by using its data reader
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