DASH is new ISO/IEC MPEG and 3GPP standard for HTTP multimedia streaming that begins to be widely accepted in the industry. DASH is design to be flexible and support various multimedia formats. DASH unify the propriet...
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(纸本)9781467359382
DASH is new ISO/IEC MPEG and 3GPP standard for HTTP multimedia streaming that begins to be widely accepted in the industry. DASH is design to be flexible and support various multimedia formats. DASH unify the proprietary adaptive streaming solutions and suggests differing between them by using different behavioral approaches, each one best suited for the specific streaming application. Each behavior is determined by Adaptation Logic (AL), which decides according to the estimation of the network conditions and buffer state what is the best suitable segment to be requested from the streaming server. This work presents the drawback of current DASH standard and its vulnerability to variable bit rate stream encoding. We have found that the advertised bit rate for each quality layer that was dictated by the Media Presentation Description (MPD) isn't accurate for VBR streaming. Moreover, we suggest an adaptive Buffer Moving Median (ABMM) buffer sensitive adaptation logic that will support its bandwidth estimation decisions based on the client buffer redundancy. The new method was found to be suitable .for mobile network traffic which is characterized with large fluctuations with network bandwidth. Our proposed solution showed more than 20 percent better average PSNR improvement compared to the original VLC plug-in rate adaptation logic.
This paper proposes a modification to extrapolated LMS algorithm proposed in [6] to guarantee consistent improved convergence speed. Unlike the original extrapolated LMS algorithm that exhibits improved performance on...
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This paper proposes a modification to extrapolated LMS algorithm proposed in [6] to guarantee consistent improved convergence speed. Unlike the original extrapolated LMS algorithm that exhibits improved performance on specific runs but is not guaranteed to perform well at any given run, the proposed algorithm is based on the repeated application of the extrapolation process in a way that allows the algorithm to retain the ELMS advantage for any arbitrary run with limited additional complexity.
In this paper, we study the behavior of the Hedge algorithm in the online stochastic setting. We prove that anytime Hedge with decreasing learning rate, which is one of the simplest algorithm for the problem of predic...
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In this paper, we study the behavior of the Hedge algorithm in the online stochastic setting. We prove that anytime Hedge with decreasing learning rate, which is one of the simplest algorithm for the problem of prediction with expert advice, is remarkably both worst-case optimal and adaptive to the easier stochastic and adversarial with a gap problems. This shows that, in spite of its small, non-adaptive learning rate, Hedge possesses the same optimal regret guarantee in the stochastic case as recently introduced adaptive algorithms. Moreover, our analysis exhibits qualitative differences with other versions of the Hedge algorithm, such as the fixed-horizon variant (with constant learning rate) and the one based on the so-called "doubling trick", both of which fail to adapt to the easier stochastic setting. Finally, we determine the intrinsic limitations of anytime Hedge in the stochastic case, and discuss the improvements provided by more adaptive algorithms.
Presents corrections to information from the article, "“Bijnan Bandyopadhyay [People in Control],” (How, J.P.), IEEE Control Syst. Mag., vol. 39, no. 3, pp. 25–30, June 2019. doi: 10.1109/MCS.2019.2900817.
Presents corrections to information from the article, "“Bijnan Bandyopadhyay [People in Control],” (How, J.P.), IEEE Control Syst. Mag., vol. 39, no. 3, pp. 25–30, June 2019. doi: 10.1109/MCS.2019.2900817.
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