The newly adopted scalable extension of H.264/AVC video coding standard (SVC) demonstrates significant improvements in coding efficiency in addition to an increased degree of supported scalability relative to the scalable profiles of prior video coding standards. Due to the complicated hierarchical prediction structure of the SVC and the concept of key pictures, content-aware rate adaptation of SVC bit streams to intermediate bit rates is a nontrivial task. The concept of quality layers has been introduced in the design of the SVC to allow for fast content-aware prioritized rate adaptation. However, existing quality layer assignment methods are suboptimal and do not consider all network abstraction layer (NAL) units from different layers for the optimization. In this paper, we first propose a technique to accurately and efficiently estimate the quality degradation resulting from discarding an arbitrary number of NAL units from multiple layers of a bitstream by properly taking drift into account. Then, we utilize this distortion estimation technique to assign quality layers to NAL units for a more efficient extraction. Experimental results show that a significant gain can be achieved by the proposed scheme.
The emergence of 3G and 4G wireless networks brings with it the possibility of streaming high quality video content on-demand to mobile users. Wireless video applications require appropriate scheduling techniques that make use of the specific characteristics of video content, as well as the well known gains from multiuser diversity. While fast and frequent channel feedback is available in the new generation of wireless networks, the channel estimates cannot be perfect, and channel losses should be taken into account in the packet scheduling and resource allocation. The proposed scheme is formulated as a joint optimization over the resource allocation and channel loss protection, in order to minimize the distortion of the received video sequences. The distortion is a function of the packets deliberately dropped at the transmission queue due to congestion, as well as of random channel losses. The scheme makes use of a packet prioritization strategy that orders video packets based on their contribution to reducing the expected distortion of the received video sequence. Simulation results show that the proposed technique significantly outperforms content-independent packet scheduling schemes.
Demand for multimedia services, such as video streaming over wireless networks, has grown dramatically in recent years. The downlink transmission of multiple video sequences to multiple users over a shared resource-limited wireless channel, however, is a daunting task. Among the many challenges in this area are the time-varying channel conditions, limited available resources, such as bandwidth and power, and the different transmission requirements of different video content. This work takes into account the time-varying nature of the wireless channels, as well as the importance of individual video packets, to develop a cross-layer resource allocation and packet scheduling scheme for multiuser video streaming over lossy wireless packet access networks. Assuming that accurate channel feedback is not available at the scheduler, random channel losses combined with complex error concealment at the receiver make it impossible for the scheduler to determine the actual distortion of the sequence at the receiver. Therefore, the objective of the optimization is to minimize the expected distortion of the received sequence, where the expectation is calculated at the scheduler with respect to the packet loss probability in the channel. The expected distortion is used to order the packets in the transmission queue of each user, and then gradients of the expected distortion are used to efficiently allocate resources across users. Simulations show that the proposed scheme performs significantly better than a conventional content-independent scheme for video transmission.
In this paper a new content-based copy identification method for video sequences is presented. It is robust to a number of image transformations and particulary robust to compression artifacts. A scale and rotation invariant local image descriptor for corner points in detected key frames is proposed based on a generalized Radon transform. In addition, a distance similarity metric is used that fuses intensity and geometry information to compare key frames extracted using a scene detection algorithm. Furthermore, to achieve low querying computational complexity a DP approach is employed. Experimental results demonstrate the effectiveness of our approach.
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