A rule-based intelligent multimedia streaming server system

In this paper, a novel multimedia transmission server is designed using rule based expert system technology. It is superior on current media transmission servers because it is more powerful on streaming, flexible on control, and reliable on maintenance. In the proposed expert server, working parameters and management methods are separated from the decision-making procedure and stored in an xml database called knowledge base. The server fires different transmission strategies inside the knowledge base by runtime inference. Thus it can easily adjust transmission parameters under various environments and adopt new developed methods without significantly changing the main body of server codes. In this paper, we not only analyzed the time complexity of inference procedure and the real time characteristics of the server, but also tested the server performance on local area networks. Results showed that the expert system can deliver smooth streams with around 50% deduced throughput oscillations when compared with a single rate control method. The saved 50% bandwidth could be used for supporting more users. When congestion happened, the expert system reacts intelligently and conducts cooperative steps to relocate its resources. At the same time, the congestion related information is recorded and referred for future congestion avoidance. Attractively, these enhanced performances are achieved by taking less than 10% of the CPU time for the execution of the expert server control program.

[1]  Suzanne Smith,et al.  Verification and validation of rule-based expert systems , 1993 .

[2]  Remco C. Veltkamp,et al.  A Survey of Music Information Retrieval Systems , 2005, ISMIR.

[3]  Richard Forsyth,et al.  Expert systems: Principles and case studies , 1984 .

[4]  Dmitri Loguinov,et al.  Multi-layer active queue management and congestion control for scalable video streaming , 2004, 24th International Conference on Distributed Computing Systems, 2004. Proceedings..

[5]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[6]  E. J. Zakrzewski,et al.  Applications of expert systems to network control , 1988, IEEE International Conference on Communications, - Spanning the Universe..

[7]  Ning Hu,et al.  Polyphonic Audio Matching for Score Following and Intelligent Audio Editors , 2003, ICMC.

[8]  Meinard Müller,et al.  Content-Based Retrieval in Digital Music Libraries , 2004, ECDL.

[9]  Nirwan Ansari,et al.  Throughput of high-speed TCP in optical burst switching networks , 2005 .

[10]  Jin Wu,et al.  A Fuzzy-Expert-System-Based Structure for Active Queue Management , 2003, ICIC.

[11]  Dmitri Loguinov,et al.  Oscillations and buffer overflows in video streaming under non-negligible queuing delay , 2004, NOSSDAV '04.

[12]  Nicola Orio,et al.  Score Following: State of the Art and New Developments , 2003, NIME.

[13]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[14]  Bernhard Plattner,et al.  Media- and TCP-friendly congestion control for scalable video streams , 2006, IEEE Transactions on Multimedia.

[15]  Gerhard Widmer,et al.  MATCH: A Music Alignment Tool Chest , 2005, ISMIR.

[16]  Xavier Rodet,et al.  Improving polyphonic and poly-instrumental music to score alignment , 2003, ISMIR.

[17]  Meinard Müller,et al.  Automatic synchronization of music data in score-, MIDI- and PCM-format , 2003, ISMIR.

[18]  Van Jacobson,et al.  Random early detection gateways for congestion avoidance , 1993, TNET.

[19]  Xiaofei Zhou,et al.  Discrete LQ rate control schedule system for multimedia transmission , 2006, 20th International Conference on Advanced Information Networking and Applications - Volume 1 (AINA'06).

[20]  Jimmy Troost,et al.  Dealing with high workload in future naval command and control systems , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[21]  Dmitri Loguinov,et al.  Analysis of rate-distortion functions and congestion control in scalable internet video streaming , 2003, NOSSDAV '03.

[22]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[23]  Meinard Müller,et al.  Towards an Efficient Algorithm for Automatic Score-to-Audio Synchronization , 2004, ISMIR.

[24]  Jianping Pan,et al.  Performance analysis of TCP-friendly AIMD algorithms for multimedia applications , 2005, IEEE Transactions on Multimedia.

[25]  Antonello D'Aguanno,et al.  Automatic Synchronisation Between Audio and Score Musical Description Layers , 2007, SAMT.

[26]  Theodoros N. Arvanitis,et al.  Fair weighted round robin scheduling scheme for DiffServ networks , 2003 .

[27]  J. Stephen Downie,et al.  Music information retrieval , 2005, Annu. Rev. Inf. Sci. Technol..

[28]  Seong-Ryong Kang,et al.  Delayed stability and performance of distributed congestion control , 2004, SIGCOMM 2004.

[29]  Lisong Xu.  Extending equation-based congestion control to high-speed and long-distance networks , 2007, Comput. Networks.

[30]  Daniel P. W. Ellis,et al.  Ground-truth transcriptions of real music from force-aligned MIDI syntheses , 2003, ISMIR.

[31]  M. Malek,et al.  An expert system-based approach to capacity allocation in a multiservice application environment , 1991, IEEE Network.