3D video applications are growing increasingly common as the required infrastructure and technology to stream 3D video becomes more predominant. However, the quality of displayed videos may fluctuate due to packet failure as an integral part of either wired or wireless streaming networks. Therefore, more robust methods of video streaming have always been fascinating to show more favourable efficiency outcomes. This thesis first examines different video streaming techniques and compares the pros and cons of each technique. It then introduces a new streaming method that applies to 3D video for live video streaming applications especially for a sporting event or other live video applications. To this end, the thesis describes how a 3D video is captured and represented, and how humans perceive the 3D scene. Considering the pros and cons of current video streaming techniques and intended applications, the proposed method introduces a new multiple description coding (MDC) method focusing on interesting objects of the scene, called the region of interest (ROI). It is worth mentioning that a new technique, using the scene's depth information, is used to extract the ROI. This technique is not as complex as learning algorithms are, and there is no need to train the algorithm. Since the human eye is more sensitive to objects than pixels, this method can also provide better performance from the point of subjective assessment (which is out of focus of this thesis) because the proposed method focuses on important objects of the scene and assigns more bandwidth to them.