Application of Neural Fuzzy Controller for Streaming Video over IEEE 802.15.1

This paper is to introduce an application of Artificial Intelligence (AI) to Moving Picture Expert Group-4 (MPEG-4) video compression over IEEE.802.15.1 wireless communication in order to improve quality of picture. 2.4GHz Industrial, Scientific and Medical (ISM) frequency band is used for the IEEE 802.15.1 standard. Due to other wireless frequency devices sharing the same carrier, IEEE 802.15.1 can be affected by noise and interference. The noise and interference create difficulties to determine an accurate real-time transmission rate. MPEG-4 codec is an “object-oriented” compression system and demands a high bandwidth. It is therefore difficult to avoid excessive delay, image quality degradation or data loss during MPEG-4 video transmission over IEEE 802.15.1 standard. Two buffers have been implemented at the input of the IEEE 802.15.1 device and at the output respectively. These buffers are controlled by a rule based fuzzy logic controller at the input and a neural fuzzy controller at the output. These rules manipulate and supervise the flow of video over the IEEE 802.15.1 standard. The computer simulation results illustrate the comparison between a non-AI video transmission over IEEE 802.15.1 and the proposed design, confirming that the applications of intelligent technique improve the image quality and reduce the data loss.

[1]  Jaap C. Haartsen,et al.  The Bluetooth radio system , 2000, IEEE Personal Communications.

[2]  Hassan B. Kazemian,et al.  An adaptive control for video transmission over bluetooth , 2006, IEEE Transactions on Fuzzy Systems.

[3]  Rodger E. Ziemer,et al.  Principles of communications : systems, modulation, and noise , 1985 .

[4]  H.B. Kazemian,et al.  An Integrated Neuro-Fuzzy Approach to MPEG Video Transmission in Bluetooth , 2007, 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing.

[5]  Vasos Vassiliou,et al.  Adaptive Feedback Algorithm for Internet Video Streaming based on Fuzzy Rate Control , 2007, 2007 12th IEEE Symposium on Computers and Communications.

[6]  Andreas Pitsillides,et al.  Fuzzy logic controlled RED: congestion control in TCP/IP differentiated services networks , 2003, Soft Comput..

[7]  Athanasios V. Vasilakos,et al.  Evolutionary-fuzzy prediction for strategic QoS routing in broadband networks , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[8]  Rouzbeh Razavi,et al.  Fuzzy Logic Control of Adaptive ARQ for Video Distribution over a Bluetooth Wireless Link , 2007, Adv. Multim..