Multimedia Data Transmission over Wired/Wireless Networks
Bharat Bhargava
Gang Ding, Xiaoxin Wu, Mohamed Hefeeda, Halima Ghafoor
Purdue University
Website: http://www.cs.purdue.edu/homes/bb E-mail: bb@cs.purdue.edu
Phone: 765-494-6702
Research Issues
Multimedia transmission
High data rate
Time sensitive
Networks
Variable or limited bandwidth
Time delay
Packet loss
Multimedia over wired/wireless networks
Error resilient data transport control
Seamless transmission over hybrid networks
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Multimedia Data Transport Control
Cross-layer rate control
Match multiple data streams to the available bandwidth
Cross-layer error control
Adaptively update upper layer error control parameters based on the current network condition
Data Link Layer E rror detec tion Retrans mis s ion Application Layer
Data c ompres s ion
Cross- layer Control Module
Physical Layer
S1
S2
S3 S4 S5 S6
S7 Raw data
Transport Layer
B1
B2 B4
B3
R1 Data Link Layer
E rror detec tion Retrans mis s ion Application Layer
Data dec ompres s ion
Physical Layer R4
R3
R7 R2
R6 R5
Cross- layer Control Module
Transport Layer Raw data
B: rate control, S: sender’s rate control, R: receiver’s rate control
Multimedia Data Transport Control
28.8858 31.7590
26.8716 36.618
94.889 34.287
69.889 37.199
Terminator I
25.4033 31.2513
26.6107 36.340
94.889 35.065
76.611 36.782
The Firm
29.2075 31.0195
26.4372 35.841
89.000 34.386
62.611 36.595
Star W ars V
10.0810 31.3415
29.6585 34.058
40.887 33.697
30.665 37.975
Silence of Lambs
27.2220 32.4610
28.6769 35.649
88.444 33.941
64.444 36.661
Jurassic Park I
26.5270 25.6563
21.8664 30.385
38.221 29.761
28.944 36.107
Aladdin
29.6640 33.7067
29.1975 37.564
97.556 36.825
88.333 37.791
Citizen kane
14.598 32.9462
30.1252 37.088
98.222 35.711
80.055 37.225
Star W ars IV
Parity data R ( Byte) Received PSNR
( dB) Received PSNR
( dB) Sent PSNR
( dB) Sent enhancement
layer (%) Sent PSNR
( dB) Sent enhancement
layer (%)
Our approach:
Cross-layer rate + error control Our approach:
Cross-layer rate control Regular approach:
Link layer rate control
Original Peak Signal Noise Ratio
(dB)
Movie
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Multimedia Data Transport Control
G. Ding, B. Bhargava and X. Wu, and “Cross-Layer Algorithms for Video Transmission over Wireless Networks,” In Handbook of Algorithms for Mobile and Wireless Networking and Computing, CRC Press, 2005.
Video transmission over wireless networks is challenging due to the time-varying bandwidth and the error-prone wireless channel. This paper reviews the state-of- the-art research on video compression algorithms and network protocols to
improve quality of service. The cross-layer network control algorithms are proposed, in which the lower layers of wireless networks cooperate with the application layer to adjust error control strategy and transmission rate. The theoretical analysis and simulation results show that, with the inter-layer
communication, the proposed algorithms can significantly improve the efficiency of error control and the accuracy of transmission rate selection. The
implementation issues of applying the proposed algorithms to 3G mobile networks and IEEE 802.11 wireless local area networks are discussed.
G. Ding, H. Ghafoor and B. Bhargava, “Resilient Video
Transmission over Wireless Networks,” In IEEE International
Conf. on Object-oriented Real-time Distributed Computing, Japan, May 2003.
Proposes an error resilient video transmission architecture over mobile wireless networks. Radio link layer, transport layer, and application layer are combined to deal with high error rate in wireless environments. The algorithms for both
sender and receiver are given. An adaptive algorithm is further presented to automatically adjust parity data length in error control. The performance of the proposed algorithm is analyzed by both theory and experimental results.
References
B. Bhargava, S. Wang, M. Khan and A. Habib,
“Multimedia Data Transmission and Control using Active Networks,” Journal of Computer
Communications, 2005
B. Bhargava, C. Shi and Y. Wang, “MEPG Video
Encryption Algorithms,” Journal of Multimedia Tools and Applications, 24, 57-79, 2004.
G. Ding, B. Bhargava and X. Wu, and “Cross-Layer Algorithms for Video Transmission over Wireless Networks,” In Handbook of Algorithms for Mobile and Wireless Networking and Computing, CRC Press, 2005.
G. Ding, H. Ghafoor and B. Bhargava, “Resilient Video Transmission over Wireless Networks,” In IEEE
International Conf. on Object-oriented Real-time Distributed Computing, Japan, May 2003.
Multimedia Data Transport Control
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Multimedia Transmission over
Hybrid Networks (Planed Research)
To transport multimedia data over both back-bone and wireless networks, an
intermediate Proxy or agent can be used
Located at the junction of backbone wired network and wireless networks
Performs media format transformation
Dynamically collects network condition from various wireless links
Makes adaptive QoS control, scheduling, and
caching of multimedia data transmitted at
different rates
Adaptable Video Conferencing
Video conferencing systems (VCS) have become practical in commercial and research institutions because the advances of technologies in
networking and multimedia applications. A video conferencing session involves multiple parties, possibly geographically interspersed, which exchange real-time video data. However, anomalies such as site failure and network partitioning affect the effectiveness and utilization of the communication capabilities. Video conferencing systems lack the ability of dynamically adapting themselves to the variations in the system resources such as network bandwidths, CPU utilization, memory and disk storage. In VCS, changes in parameters such as frame sizes, codec schemes, color depths, and frame resolutions can only be made by users. They cannot be made based on the system measurements of currently available resources.
We need to limit the users' burden in keeping the system running in the most suitable mode to current environment and make it possible to provide the best possible service based on the status of the system.
Incorporating adaptability into a video conferencing systems minimizes the effects of the variations in system environments on the quality of video conference sessions.
In the report, we discuss the concept of adaptability and the basic idea for achieving adaptability in a video conferencing system, give a description of common anomalies encountered in a distributed system, review the NV video conferencing system, the testbed of our experiments, describe the
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Adaptable Video Conferencing
A video conferencing system should provided some policies and mechanisms to make it adaptable to the anomalies based on the available resources. The
advantages of the adaptability schemes for VC system include:
Heterogeneity: A VC system will adapt to heterogeneous environments. That is, a video
conference session can be held on different hardware platforms and different networks.
Scalability: A VC system will adapt itself as more users, more sites join a video conference in progress.
Anomaly Management: A VC system will adapt to anomalies and degrade gracefully when available resources decrease or become unavailable.
Resource Management: A VC system can make efficient use of resources like storage, CPU time, and communication bandwidth.
Adaptable Video Conferencing
T imelines s /A c c urac y/P rec is ion (T A P ) c annot be maintained at the highes t level s imultaneous ly during anomalies . We mus t trade
among thes e attribute values . T he polic y to trade among thes e attributes c an be des c ribed as follows :
M aintaining T imelines s when Bandwidth Dec reas es
Reduce frame size (The accuracy is maintained unless the frame size is below a certain value).
Reduce frame resolution (Both accuracy and precision are reduced).
Dither color frame to black and white.
Compress color depth.
Switch to a codec scheme that has a higher compression ratio (Side effect: CPU utilization increases. This can be compensated by frame resizing and resolution reduction).
M aintaining A c c urac y when Bandwidth Dec reas es
Switch to a lossless codec scheme with reduced frame size.
Dither color frame to black and white.
Compress color depth (compress Y and UV no more than 2 bits each).
Do not use lossy codec schemes.
Do not reduce frame size or resolution by a big factor.
M aintaining T imelines s when C P U U tilization I nc reas es
Switch to a codec scheme that requires less computation (usually with lower compression ratio).
Reduce frame size.
Dither color frame to black and white.
Do not compress color depth.
Do not reduce frame resolution.
M aintaining A c c urac y when C P U U tilization I nc reas es
Switch to a lossless codec scheme
Reduce frame size.
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Adaptable Video Conferencing
References
B. Bhargava, “Adaptable Video Conferencing”.
B. Bhargava and S. Li, “Exploring Adaptability for Video Conferencing”.
Peer-to-peer Multimedia Streaming
PROMISE: PeertoPeer Media Streaming Using CollectCast
Peer lookup
Peer-based aggregated streaming
Dynamic adaptation to network conditions
PROMISE is based on a new application level peer-to- peer service: CollectCast
Inferring and leveraging the underlying network topology and performance
Monitoring the status of peers and connections and reacting to peer/connection failure or degradation with low overhead
Dynamically switching active senders and standby senders so that the collective network performance out of the active senders remains satisfactory
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Peer-to-peer Multimedia Streaming
Assignment Rate/Data
Inference and Labeling Topology
Candidate set
Monitoring and Adapatation
CollectCast
Distributed Streaming Application (PROMISE)
Active set
Active, Rates
Annotated topology
Peer Selection
Switch peers
Peer−to−Peer Lookup Substrate (Pastry)
Redistribute rates
Peer-to-peer Multimedia Streaming
References
A. Habib, S. Fahmy and B. Bhargava, “On Monitoring and Controlling QoS Network Domains,” International Journal of Network Management, 2005.
M. Hefeeda, B. Bhargava and D. Yau, “A Hybrid Architecture for Cost-Effective On-Demand Media Streaming,” Journal of
Computer Networks, 44(3): 353-382, 2004.
M. Hefeeda, A. Habib, B. Botev, D. Xu, and B. Bhargava,
“PROMISE: Peer-to-Peer Media Streaming Using CollectCast,”
In Proc. of ACM Multimedia 2003, pages 45-54, Berkeley, CA, November 2003.
M. Hefeeda and B. Bhargava, “On-Demand Media Streaming Over the Internet,” In Proc. of 9th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS), pages 279-285, San Juan, Puerto Rico, May, 2003.
D. Xu, M. Hefeeda, S. Hambrush, B. Bhargava, “On Peer-to-Peer Media Streaming,” In Proc. of IEEE International Conference on Distributed Computing Systems (ICDCS), pages 363-371, Vienna, Austria, July 2002.
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