Tele-immersive systems can improve productivity and aid communication by allowing distributed parties to exchange information via a shared immersive experience. The TEEVE research project at the University of Illinois at Urbana-Champaign and the University of California at Berkeley seeks to foster the development and use of tele-immersive environments by a holistic integration of existing components that capture, transmit, and render three-dimensional (3D) scenes in real time to convey a sense of immersive space. However, the transmission of 3D video poses significant challenges. First, it is bandwidth-intensive, as it requires the transmission of multiple large-volume 3D video streams. Second, existing schemes for 2D color video compression such as MPEG, JPEG, and H.263 cannot be applied directly because the 3D video data contains depth as well as color information. Our goal is to explore from a different angle of the 3D compression space with factors including complexity, compression ratio, quality, and real-time performance. To investigate these trade-offs, we present and evaluate two simple 3D compression schemes. For the first scheme, we use color reduction to compress the color information, which we then compress along with the depth information using zlib. For the second scheme, we use motion JPEG to compress the color information and run-length encoding followed by Huffman coding to compress the depth information. We apply both schemes to 3D videos captured from a real tele-immersive environment. Our experimental results show that: (1) the compressed data preserves enough information to communicate the 3D images effectively (min. PSNR > 40) and (2) even without inter-frame motion estimation, very high compression ratios (avg. > 15) are achievable at speeds sufficient to allow real-time communication (avg. ≈ 13 ms per 3D video frame).
To achieve scalable and efficient on-demand media distribution, existing solutions mainly make use of multicast as underlying data delivery support. However, due to the intrinsic conflict between the synchronous multicast transmission and the asynchronous nature of on-demand media delivery, these solutions either suffer from large playback delay or require clients to be capable of receiving multiple streams simultaneously and buffering large amount of data. Moreover, the limited and slow deployment of IP multicast hinders their application on the Internet.
To address these problems, we propose asynchronous multicast, which is able to directly support on-demand data delivery. Asynchronous multicast is an application level solution. When it is deployed on a proxy network, stable and scalable media distribution can be achieved. In this paper, we focus on the problem of efficient media distribution. We first propose a temporal dependency model to formalize the temporal relations among asynchronous media requests. Based on this model, we propose the concept of Media Distribution Graph (MDG), which represents the dependencies among all asynchronous requests in the proxy network. Then we formulate the problem of efficient media distribution as finding Media Distribution Tree (MDT), which is the minimal spanning tree on MDG. Finally, we present our algorithm for MDT construction/maintenance. Through theoretical analysis and experimental study, we claim that our solution can meet the goals of scalability, efficiency and low access latency at the same time.