Dynamic network resource allocation using multimedia content features and traffic features
First Claim
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1. A method for dynamically allocating network resources while transferring a bit stream in a network, comprising:
- extracting first content features from the bit stream to determine renegotiation points and observation periods, in which the bit stream is compressed;
extracting second content features and traffic features from the bit stream during the observation periods; and
combining the second content features and the traffic features to predict the network resources to be allocated at the renegotiation points.
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Abstract
A method for dynamically allocating network resources while transferring multimedia at variable bit-rates in a network extracts first content features from the multimedia to determine renegotiation points and observation periods. Second content features and traffic features are extracted from the multimedia bit stream during the observation periods. The second content features and the traffic features are combined in a neural network to predict the network resources to be allocated at the renegotiation points.
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Citations
24 Claims
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1. A method for dynamically allocating network resources while transferring a bit stream in a network, comprising:
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extracting first content features from the bit stream to determine renegotiation points and observation periods, in which the bit stream is compressed;
extracting second content features and traffic features from the bit stream during the observation periods; and
combining the second content features and the traffic features to predict the network resources to be allocated at the renegotiation points. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23)
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24. A system for dynamically allocating network resources while transferring a bit stream in a network, comprising:
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a feature extraction unit configured to extract first content features, second content features, and traffic features from the bit stream during the observation periods, in which the bit stream is compressed;
means determining renegotiation points and observation periods in the bit stream from the first content features; and
a prediction neural network configured to combine the second content features and the traffic features to predict the network resources to be allocated at the renegotiation points.
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Specification