## 2 Literature Survey
In [14], authors included min-max averaging operations in basic average loss interval algorithm, which helps to reduce the variance of the Average Loss interval (ALI) estimation by suppressing the correlation of neighboring loss intervals arising from the Additive Increase and Multiple Decrease (TCP-AIMD) behavior. In [15], authors modified some fixed parameters of TFRC throughput equation, which results in increased throughput and less packet loss. In [16] authors proposed an enhanced queue management scheme for TFRC to reduce packet loss and to increase the throughput of bursty traffic. In [17], the authors proposed an enhancement to TFRC that uses a self-clocking mechanism to eliminate packet loss during bandwidth decreased handover. In [18], the authors proposed two strategies called bandwidth estimation method and new slow start mechanism to enhance the performance of TFRC in the view of bandwidth allocation among multiple flows and packet losses caused by over shooting. In [19], the authors analyzed the performance of TFRC to transport multimedia applications over homogeneous wired networks and found that TFRC needs improvement in homogeneous wired networks. To increase the ability of TFRC, authors proposed State-TFRC (S-TFRC), which includes a feedback mechanism which exchanges link information and transmission parameters. Also, authors analyzed a Markov model to demonstrate S-TFRC protocol behavior in different events of congestion like timeouts, loss rates, and no-feedback timer expiration. NS-2.34 is used to test the environment of proposed S-TFRC and classical TFRC. Simulation results show that proposed S-TFRC produces better performance in terms of throughput, delay and transmission losses for real time applications. In [20], the authors proposed a new receiver based retransmission scheme for TFRC to improve the effective retransmission ratio according to the forward path delay variance and also reduces unnecessary retransmissions. In [21], the authors suggested a fast startup scheme and new bandwidth estimation methods to overcome the problems of TFRC RTT-unfairness problem and overshooting problem. In [22], an enhanced TFRC control algorithm called Network State Control (NSTC) was presented, which introduces available bandwidth variation as network identification and limits sending rate in different network states. This scheme enhances TFRC performance in terms of decreased packet loss and comprehensively improves QoS and friendliness. In [23], authors proposed a new congestion control framework called Ultra-driven TFRC (UTFRC), which is more suitable for multimedia streaming applications than classical TFRC. In [24], the authors proposed a new TCP variant called TCP-WELCOME that uses Loss Differentiation Algorithm (LDA) and Loss Recovery Algorithm (LRA) techniques to improve the performance over other TCP variants. They used a new methodology for finding RTO value based on old and new RTT values. In [25; 26; 27], the authors analyzed the popular asymmetric cryptographic algorithm RSA with Carmichael function, and identified that Carmichael function produces smaller private key than Euler totient function, which reduces the decryption time of the receiver to decrypt the cipher text. In [28; 29], the authors proposed fine-tuned values for adaptive TTL to improve performance under rapid changes. Results show that this modified adaptive TTL with dynamic bounds gave good performance than the original.