|
|
|
|
|
Project Description
|
|
This project focuses on the challenges when designing data transmissions
for streaming media systems (realtime audio and video).
It is motivated by the Yima continous media server project.
The Yima server is based on a cluster design. Each cluster node
provides an IP link into a distributed network fabric (e.g., Internet).
The research focuses on the following aspects of this architecture:
- Variable Bitrate (VBR) Media: Constant bitrate (CBR) encoding
of media streams can result in either reduced quality of complex scenes or
wasted storage space during simple scenes.
VBR encoding on the other hand allocates bits where they are most needed,
resulting in a more uniformly high visual or aural quality.
The disadvantage of the VBR technique is that it results in bursty network
traffic and uneven resource utilization when streaming media.
Our techniques focuses on smoothing VBR media transmissions without
a priori knowledge of the actual bitrate. Hence, our technique can be
applied to (a) live streams and (b) stored streams without requiring
any server side pre-processing.
|

|
- Retransmission-Based Error Control for Multi-Node Servers:
Large-scale continuous media (CM) system implementations require scalable
servers most likely built from clusters of storage nodes. Across such nodes random data placement is an attractive
alternative to the traditional round-robin striping. One benefit of random
placement is that additional nodes can be added with low data-redistribution
overhead such that the system remains load balanced. One of the challenges in
this environment is the implementation of a retransmission-based error control
(RBEC) technique. Because data is randomly placed, a client may not know which
server node to ask for a lost packet retransmission. We have designed and
implemented a RBEC technique that utilizes the benefits of random data
place-ment in a cluster server environment while allowing a client to
efficiently identify the correct server node for lost packet requests. We have
implemented and evaluated our technique with a one-, two-, and four-way server
cluster and across local and wide-area networks. Our results show the
feasibility and effectiveness of our approach in a real-world environment.
|
|
Research Approach
|
|
Our technique called Multi-Threshold Flow Control (MTFC)
utilizes multi-level buffer thresholds at the client side that trigger
feedback information sent to the server. This technique can be applied
to both live captured streams and stored streams without requiring any
server side pre-processing. We have implemented this scheme in our
continuous media server Yima and verified its operation across real
world LAN and WAN connections. The results show smoother transmission
schedules than any other previously proposed online technique.
|
|
People
|
|
Roger Zimmermann
- Research Assistant Professor at the USC Computer Science Department
- Email: rzimmerm@imsc.usc.edu
- Phone: (213) 740-7654
Cyrus Shahabi
Kun Fu
- Research Assistant
- Ph.D. Student
- Email: kfu@usc.edu
- Phone: (213) 740-2289
Mehrdad Jahangiri
Nitin Nahata
- Research Assistant
- Master Student
- Email: nnahata@usc.edu
- Phone: (213) 740-4177
|
|
Licensing
|
|
For licensing information please see USC's
Office of
Technology Licensing.
|
|
Relevant Papers
|
|
Get the PDF reader from
Adobe.
-
A Multi-Threshold Online Smoothing Technique for Variable Rate Multimedia
Streams.
Roger Zimmermann, Kun Fu, Cyrus Shahabi, and Mehrdad Jahangiri.
Submitted for publication.
Abstract
-
Retransmission-Based Error Control in a Many-to-Many Client-Server
Environment.
Roger Zimmermann, Kun Fu, Nitin Nahata, and Cyrus Shahabi.
Accepted for presentation at the SPIE Conference on Multimedia Computing and
Networking 2003 (MMCN 2003),
Santa Clara, California, January 29-31, 2003.
Abstract
Postscript (272 KB)
PDF (1,380 KB)
-
Streaming of DivX AVI Movies.
Roger Zimmermann.
Accepted for presentation at the ACM Symposium on Applied Computing
(SAC 2003),
Melbourne, Florida, March 9-12, 2003.
Abstract
Postscript (119 KB)
PDF (128 KB)
Maintained by
Roger Zimmermann
Last updated: Thursday January 2, 2003.
All Rights Reserved © NUS
Data Management Research Laboratory 1999 - 2007.
|
|