Title :
VBR video frame size prediction using seasonal ARIMA models
Author_Institution :
Fac. of Electr. Eng., Mech. Eng. & Naval Archit., Univ. of Split, Split, Croatia
Abstract :
Increasing quality of service (QoS) requirements demand continuous development of new and advanced traffic prediction methods. This is especially true for variable bit rate (VBR) video traffic. This paper provides insights into current approaches and solutions in the area of online frame size prediction methods for MPEG4 video, and proposes a prediction method based on seasonal ARIMA models. Proposed method is effective in frame size prediction and promises up to 30% better results than alternative methods.
Keywords :
prediction theory; quality of service; traffic; video coding; MPEG4 video; QoS; VBR video frame size prediction; continuous development; online frame size prediction method; quality of service; seasonal ARIMA model; traffic prediction method; variable bit rate video traffic; Autoregressive processes; Computational modeling; Kalman filters; Mathematical model; Predictive models; Streaming media;
Conference_Titel :
Software, Telecommunications and Computer Networks (SoftCOM), 2012 20th International Conference on
Conference_Location :
Split
Print_ISBN :
978-1-4673-2710-7
