Abstract
Time critical nature of the real-time communication usually makes connection-oriented protocols such as TCP useless, because retransmission of old and probably expired packets is not desired. However, connectionless protocols such as UDP do not provide such packet loss control and suitable for real-time communication such as voice or video communication. In this paper, we present an adaptive approach for the intelligent packet loss control for connectionless real-time voice communication. Instead of detecting and resending lost voice packets, this heuristic estimates the packet loss rate adaptively using a modified version of reinforcement learning and resends the most critical packets before they are expired. Our simulations indicate that this approach is promising for a remarkable improvement in QoS of real-time voice communication.
Highlights
Today, real-time communication is getting more focus from both the academic community and industry
We present an adaptive approach for the intelligent packet loss control for connectionless realtime voice communication
After a time of unsuccessful retransmission, TCP gives up retransmission of a lost packet
Summary
Real-time communication is getting more focus from both the academic community and industry. Connectionless protocols providing no retransmissions of lost packets, such as UDP, are usually used in real-time communication such as voice or video communication. Packets are retransmitted on the time before they are expired This algorithm is designed to work with connectionless protocols such as RTP over UDP. There are different queues in the routers and each queue has a different QoS requirements They use reinforcement learning to schedule packets in the queues so that quality constraint in terms of delay for each queue is attained. Wolpert et al use collective intelligence to route Internet traffic by introducing RL-based agents on the routers 5, 6 They show that at its best settings, their RL-based routing algorithm achieves throughputs up to three and one half time better than that of the standard Belman-Ford routing algorithm 6.
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