Traffic Controller for Handling Service Quality in Multimedia Network

Traffic Controller for Handling Service Quality in Multimedia Network

Manjunath Ramachandra (Philips - Bangalore, India) and Vikas Jain (Philips – Bangalore, India)
DOI: 10.4018/978-1-61520-791-6.ch006
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The present day Internet traffic largely caters for the multimedia traffic throwing open new and unthinkable applications such as tele-surgery. The complexity of data transactions increases with a demand for in time and real time data transfers, demanding the limited resources of the network beyond their capabilities. It requires a prioritization of data transfers, controlled dumping of data over the network etc. To make the matter worse, the data from different origin combine together imparting long lasting detrimental features such as self similarity and long range dependency in to the traffic. The multimedia data fortunately is associated with redundancies that may be removed through efficient compression techniques. There exists a provision to control the compression or bitrates based on the availability of resources in the network. The traffic controller or shaper has to optimize the quality of the transferred multimedia data depending up on the state of the network. In this chapter, a novel traffic shaper is introduced considering the adverse properties of the network and counteract with the same.
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The multimedia traffic over the Internet has characteristic requirements and features such as large buffers, real time data transfers, user interaction and monitoring, bursty traffic etc. The traffic has a few Predictable parameters in statistical sense that should be made use for effective control. The traffic pattern obeys poisson distribution. The multimedia traffic is inherently bursty and time varying due to different degree of compression for the data of a unit time. The burstiness of one flow affects other adaptive flows. This property is used in traffic control.

The multimedia data sources are often modeled as on-off sources. The overlapping of independent on-off sources leads to arrival pattern distribution with heavy-tailed autocorrelation function. This heavy tail distribution imparts Long range dependency to the traffic. As a result, there is no flattening of the distribution towards a mean when zoomed out in time scale.

As a result of burstiness or Long-range dependency, the resources would get flooded resulting in packet losses for a long duration. The other problems of long range dependency include jitter, Delay, large buffer requirements, congestion etc. As a fall out of these issues, it would be nearly impossible to meet the committed quality of service.

In a home network scenario, there will be limited support to overcome congestion. The best way to fix this issue is to avoid the congestion to happen. So, a proactive control algorithm is required than an active one (Hollot,C., Misra,V., Towsley,D., & Gong,W, 2001). A good controller has to foresee the trends in the network traffic variations and provide inputs to the traffic source well in advance as shown in Figure 1.

Figure 1.

Traffic controller


The source would get sufficient time to adjust the traffic rate or provide sufficient redundancies with the appropriate channel coding schemes so that it would not flood the channel when it is disturbed. In this chapter, the proactive queue management model GREEN (Wu-chun Feng; Kapadia, A., & Thulasidasan, S) together with a neural network is considered.



Meeting stringent constraints on the delay is very important in the networks supporting multimedia traffic for seamless user experience. In addition to the delay and packet losses, the variable delay suffered by the packets i.e. jitter is to be given due weightage. Analysis shows that the jitter degrades the perceptual quality as much as the packet loss (Mark Claypool, Jonathan Tanner,1999).

To support the QoS in the Internet, the IETF has defined two architectures:

  • • The Integrated Services (Intsev) and

  • • Differentiated Services (Diffserv).

They have important differences in both the service definition and the implementation architectures. At the service definition level, the Intserv provides end-to-end guarantees or controlled load service (El-Haddadeh, R., Taylor, G.A., & Watts, S.J, 2004) on a per flow basis, while the Diffserv provides a coarser level of service differentiation among a small number of traffic classes.


In an Intserv (Shioda, S., Mase, K, 2005), all transactions happen on per flow basis. The routers generate and process the signals for forwarding the data and maintaining the QoS in each flow. The activities such as classification, scheduling and buffer management happens for each flow. Performing the per-flow management inside the network affects the network scalability and the performance. The IntServ although provides the agreed QoS guarantees, it results in under utilization of the resources. The scheme poses scalability issues.

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