Scalable video coding (SVC) is currently developed as an extension of H.264/AVC In the SVC encoder, an exhaustive search technique is employed to select the best coding mode for each macro block (MB). This technique a...
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Scalable video coding (SVC) is currently developed as an extension of H.264/AVC In the SVC encoder, an exhaustive search technique is employed to select the best coding mode for each macro block (MB). This technique achieves an optimal trade-off between rate and distortion, but it requires an extremely large encoding time. In this paper, we propose a fast mode decision algorithm for inter-layer coding at the enhancement layer. The proposed algorithm predicts the mode of each MB at the enhancement layer using the modes of a co-located MB and its neighboring MBs at the base layer. The proposed method can achieve a time saving of up to 74% in spatial scalability and 63% in coarse grain quality scalability with negligible loss of quality and bit rate increment
Video streaming generates a substantial fraction of the traffic on the internet. The demands of video streaming also increase the workload on the video server, which in turn leads to substantial slowdowns. In order to...
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Video streaming generates a substantial fraction of the traffic on the internet. The demands of video streaming also increase the workload on the video server, which in turn leads to substantial slowdowns. In order to resolve the slowdown problem, and to provide a scalable and robust infrastructure to support on-demand streaming, helper-assisted video-on-demand (VoD) systems have been introduced. In this architecture, helper nodes, which are micro-servers with limited storage and bandwidth resources, download and store the user-requested videos from a central server to decrease the load on the central server. Multi-layer videos, in which a video is divided into different layers, can also be used to improve the scalability of the system. In this paper, we study the problem of utilizing the helper nodes to minimize the pressure on the central servers. We formulate the problem as a linear programming using joint inter-and intra-layer network coding. Our solution can also be implemented in a distributed manner. We show how our method can be extended to the case of wireless live streaming, in which a set of videos is broadcasted. Moreover, we extend the proposed method to the case of unreliable connections. We carefully study the convergence and the gain of our distributed approach.
Video streaming is one of the dominant forms of traffic on the internet. This increases workload on the video servers, which leads to substantial slowdowns. In order to resolve the slowdown problem, and to provide a s...
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ISBN:
(纸本)9780769551043
Video streaming is one of the dominant forms of traffic on the internet. This increases workload on the video servers, which leads to substantial slowdowns. In order to resolve the slowdown problem, and to provide a scalable and robust infrastructure to support on-demand streaming, helper-assisted video-on-demand (VoD) systems have been introduced. In this architecture, helper nodes, which are micro-servers with limited storage and bandwidth resources, download and store the user-requested videos from a central server to decrease the load on the central server. Multi-layer videos, in which a video is divided into different layers, can also be used to improve scalability. In this paper, we study the problem of utilizing the helper nodes to minimize the pressure on the central servers. We formulate the problem as a linear programming (LP) optimization using joint inter-and intra-layer network coding (NC). We show that a lightweight triangular inter-layer NC can be used, instead of the general form of inter-layer NC, to achieve the optimal solution. Our solution can also be implemented in a distributed manner. We show how our method can be extended to the case of wireless live streaming, in which a set of videos is broadcast. We carefully study the convergence and the gain of our distributed approach.
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