Traffic prediction methods for quality improvement of adaptive video

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A Fast Non-Linear Adaptive Algorithm for Video Traffic Prediction

To guarantee quality of service (QoS), the requirements for video transmission such as delay and cell loss rate (CLR) are very stringent. These constraints are difficult to meet if high network utilization is desired. Dynamic bandwidth allocation is thus needed. Video traffic prediction can play an important role in dynamic bandwidth allocation and traffic management in highspeed networks. In t...

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Adaptive Online Traffic Flow Prediction Using Aggregated Neuro Fuzzy Approach

Short term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. Although various methodologies have been applied to forecast traffic parameters, several researchers have showed that compared with the individual methods, hybrid methods provide more accurate results . These results made the hybrid tools and approaches a more common method for ...

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Adaptive Linear Prediction Augmented Autoregressive Integrated Moving Average Based Prediction for VBR Video Traffic

Problem statement: Network traffic prediction plays a vital role in the optimal resource allocation and management in computer networks. This study introduces an ARIMA based model augmented by Adaptive Linear Prediction (ALP) for the real time prediction of VBR video traffic. The synergy of the two can successfully address the challenges in traffic prediction such as accuracy in prediction, res...

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Adaptive Prediction Block Filter for Video Coding

© 2012 ETRI Journal, Volume 34, Number 1, February 2012 In this letter, we propose a new prediction block filter that can reduce errors between the original and prediction blocks. The proposed filter adaptively adjusts filter coefficients by using the previously reconstructed adjacent blocks and their prediction blocks. Then, the filter is selectively applied to the current prediction block acc...

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ژورنال

عنوان ژورنال: Multimedia Systems

سال: 2017

ISSN: 0942-4962,1432-1882

DOI: 10.1007/s00530-017-0574-5