End-to-end congestion control is an important reason why the Internet is robust, scalable and simple to use. Unfortunately, purely end-to-end congestion control algorithms are incapable of preventing the unfair bandwidth allocations and congestion collapse caused by unresponsive applications, which are becoming increasingly prevalent in the Internet. In this paper, we propose a new mechanism called Fair Queueing with Feedback-based Policing (FQFP) to address unfair bandwidth allocation and congestion collapse in the Internet. We demonstrate the promise of FQFP through simluations and suggest ways in which FQFP may leverage the mechanisms currently being developed in the context of differentiated services. The FQFP mechanism is compliant with the Internet philosophy of keeping router implementations simple and pushing complexity toward the edges of the network.

Layered encoding is often recommended as a solution to the problem of varying bandwidth constraints in video multicast applications. However, multi-layered encoding of video is not sufficient to provide ideal video quality and bandwidth utilization, because network bandwidth constraints change over time. Adaptive techniques are required. In this paper we consider a rate-based Source-Adaptive Multi-layered Multicast (SAMM) algorithm that operates on an end-to-end basis. Video senders adapt the rates of their video layers in response to congestion feedback from receivers. To prevent feedback implosion, the use of computationally simple feedback merging servers is proposed. Network switches and routers are not required to implement complex flow or congestion control algorithms. The performance of the end-to-end SAMM algorithm is evaluated through simulations and compared with that of a non-source-adaptive mechanism.

Layered transmission of data is often recommended as a solution to the problem of varying bandwidth constraints in multicast applications. In the case of video multicast, this technique encodes multiple interdependent layers of video at arbitrary target rates in order to address heterogenous bandwidth constraints between the source and multiple receivers. However, multi-layered encoding alone is not sufficient to provide high video quality and high bandwidth utilization, because bandwidth constraints change over time. Adaptive techniques capable of adjusting the rates of video layers are required to maximize video quality and network utilization.

In this paper we define a class of algorithms known as Source-Adaptive Multi-layered Multicast (SAMM) algorithms. In SAMM algorithms, the source uses congestion feedback to adjust the number of generated layers and the bit rate of each layer. We contrast two specific SAMM algorithms: and end-to-end algorithm, in which only end systems monitor available bandwidth and report the amount of available bandwidth to the source, and a network-based algorithm, in which intermediate nodes also monitor and report available bandwidth. Using simulations which incorporate actual multi-layered video codecs, we demonstrate that SAMM algorithms exhibit better scalability and responsiveness to congestion than algorithms which are not source-adaptive. We also study the performance trade-offs between end-to-end and network-based SAMM algorithms.

The end-to-end nature of Internet congestion control is an important factor in its scalability and robustness. However, end-to-end congestion control algorithms alone are incapable of preventing the congestion collapse and unfair bandwidth allocations created by applications which are unresponsive to network congestion. In this paper, we propose and investigate a new congestion avoidance mechanism called Network Border Patrol (NBP). NBP relies on the exchange of feedback between routers at the borders of a network in order to detect and restrict unresponsive traffic flows before they enter the network. The NBP mechanism is compliant with the Internet philosophy of pushing complexity toward the edges of the network whenever possible. Simulation results show that NBP network whenever possible. Simulation results show that NBP effectively eliminates congestion collapse, and that, when combined with fair queueing, NBP achieves approximately max-min fair bandwidth allocations for competing network flows.