In a typical video application, such as video-on-demand, videos are continuously streamed from a video server to a distributed set of receivers. The constant-quality video compression technique commonly used, variable bit rate (VBR) encoding, produces flows with multiple time-scale rate variability, so smoothing the VBR video traffic within an entire distribution tree presents a challenging task. This paper proposes a novel wavelet-based traffic smoothing (WTS) algorithm. Unlike existing algorithms, the WTS algorithm considers traffic smoothing at multiple resolutions. It results in a pruned version of a full tree, which corresponds to the original VBR traffic. Theoretical analysis and numerical evaluation demonstrate that: 1) WTS performs well across several metrics in smoothing bursty traffic and 2) for a video bit stream with N frames, the computational complexity of WTS is O(NlogN).

Analyzes adaptive algorithm 3 (AA3) of adaptive self-organizing concurrent systems (ASOCS) and proves that AA3 correctly fulfills the rules presented. Several different models for ASOCS have been developed. AA3 uses a distributed mechanism for implementing rules so correctness is not obvious. An ASOCS is an adaptive network composed of many simple computing elements operating in parallel. An ASOCS operates in one of two modes: learning and processing. In learning mode, rules are presented to the ASOCS and incorporated in a self-organizing fashion. In processing mode, the ASOCS acts as a parallel hardware circuit that performs the function defined by the learned rules.>