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IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 51, NO. 8, AUGUST 2003 2177 Prediction of MPEG-Coded Video Source Traffic
 

Summary: IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 51, NO. 8, AUGUST 2003 2177
Prediction of MPEG-Coded Video Source Traffic
Using Recurrent Neural Networks
Aninda Bhattacharya, Alexander G. Parlos, Senior Member, IEEE, and Amir F. Atiya, Senior Member, IEEE
Abstract--Predicting traffic generated by multimedia sources is
needed for effective dynamic bandwidth allocation and for multi-
media quality-of-service (QoS) control strategies implemented at
the network edges. The time-series representing frame or visual
object plane (VOP) sizes of an MPEG-coded stream is extremely
noisy, and it has very long-range time dependencies. This paper
provides an approach for developing MPEG-coded real-time video
traffic predictors for use in single-step (SS) and multistep (MS) pre-
diction horizons. The designed SS predictor consists of one recur-
rent network for -VOPs and two feedforward networks for -
and -VOPs, respectively. These are used for single-frame-ahead
prediction. A moving average of the frame or VOP sizes time-se-
ries is generated from the individual frame sizes and used for both
SS and MS prediction. The resulting MS predictor is based on re-
current networks, and it is used to perform two-step-ahead and
four-step-ahead prediction, corresponding to multistep prediction

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology
Parlos, Alexander - Department of Mechanical Engineering, Texas A&M University

 

Collections: Computer Technologies and Information Sciences; Engineering