## 3 Proposed System  
### Enhanced RTT & RTO Calculations in TFRC  
TFRC sending rate is primarily a function of estimated average round trip time and loss event rate. TFRC sender has to estimate Retransmission Timeout (RTO) based on round trip time values. However, it does not have fine grained congestion avoidance, because round trip time rapidly changes over the network. Moreover, it increases its sending rate based on packet loss history information,but it lacks congestion avoidance feature. The RTT measure should be adaptable according to the changes in traffic. If RTO is too small, this results in unnecessary retransmission and leads to wastage of network bandwidth. If RTO is large, this results in high delay and it leads to degradation of network performance. TFRC uses the same kind of approach to find RTO for all packet losses. This same method to find RTO's are not amenable for all situations. We proposed an adaptable RTT and RTO values according to the situation. Congestion or link errors are primary causes of packet losses in the network. TFRC has to differentiate packet loss due to either congestion or link error so that packet losses can be preventable in future. To prevent or control packet loss in the network, RTT and/or RTO values should adjust dynamically. If RTT values are monotonically increasing, then there will be more chances of facing congestion in the network. To avoid packet losses in the network due to congestion, the sender has to maintain \(k\) recent RTT samples and follow condition as follows. \[\begin{array}{c  
\(\mathrm{eW_{i}}\) is the weight for each loss history. \[W_{i}=f(x)=\left\{ \begin{array}{c}1,\;1\leq i\leq n/2\\ 1-\frac{j-n/2}{n/2+1},\;n/2\leq i\leq n\end{array} \right.\]  
Generate a sequence \(\{\widetilde{S}_{i}\}_{i=1,\;2,\;3...}\) as the most recent \(k\) sequences of loss intervals derived from set \(\{S_{i}\}_{i=1,\;2,\;3...}\) and are updated on the basis of most recent sample. The minimum and maximum loss intervals are obtained using the strategy called confidence interval. This confidence interval will forecast the future loss interval and will fall under x% confidence interval. In order to find more accurate loss intervals, we replaced the minimum and maximum of most recent \(k\) sequences of loss intervals with extreme points of the confidence interval. The lower end of confidence interval can be considered as lower bound and upper end of confidence interval can be considered as upper bound for the most recent \(k\) samples \(\{S_{i}^{\sim}\}_{i=1,\;2,\;3\dots}\)  
Mathematically Confidence interval can be represented as \((\overline{X}-\mathrm{t}\,\frac{\sigma}{\sqrt{n}},\,\overline{X}+\mathrm{t} \,\frac{\sigma}{\sqrt{n}})\). Let \(\widetilde{s}_{i(\mathrm{min})}\) is the lower bound of the sequence \(\{S_{i}^{\sim}\}_{i=1,\;2,\;3\dots}\) and \(\widetilde{s}_{i(\mathrm{max})}\) is the upper bound of the sequence \(\{S_{i}^{\sim}\}_{i=1,\;2,\;3\dots}\)  
\[ \begin{array}{c}\widetilde{s}_{i(\mathrm{min})}=\;\;\;\overline{X}-\mathrm{ t}\,\frac{\sigma}{\sqrt{n}}\\ \widetilde{s}_{i(\mathrm{max})}=\;\;\;\overline{X}+\mathrm{t}\,\frac{\sigma}{ \sqrt{n}}\end{array} \right\}\,n-k+1\langle i,j_{r}\rangle n \tag{6}\]  
\(\overline{X}\) is the mean of most recent \(k\) sequences from the set \(\{S_{i}^{\sim}\}_{i=1,\;2,\;3\dots}\). t is the desired confidence coefficient value (1.96 is the default value for 95% confidence interval-t-distribution table), \(n\) is the sample size. Replace the minimum and maximum values in \(k\) most recent sample sequence \(\{S_{i}^{\sim}\}_{i=1,\;2,\;3\dots}\) with \(\widetilde{s}_{i(\mathrm{min})}\) and \(\widetilde{s}_{i(\mathrm{max})}\) respectively. The new average loss interval \(\widetilde{S}_{(1,n)}\) can be estimated from \(\{S_{i}^{\sim}\}\). Using the basic ALI method the weighted average of the last n intervals \(\widetilde{S}_{(1,n)}\) and loss event rate can be calculated as \(1/\widetilde{S}_{(1,n)}\).