RTP Control Protocol (RTCP) Feedback for Congestion Control

An effective RTP congestion control algorithm requires more fine-grained feedback on packet loss, timing, and Explicit Congestion Notification (ECN) marks than is provided by the standard RTP Control Protocol (RTCP) Sender Report (SR) and Receiver Report (RR) packets. This document describes an RTCP feedback message intended to enable congestion control for interactive real-time traffic using RTP. The feedback message is designed for use with a sender-based congestion control algorithm, in which the receiver of an RTP flow sends back to the sender RTCP feedback packets containing the information the sender needs to perform congestion control

Distributed rate allocation in switch-based multiparty videoconference

Multiparty videoconferences, or more generally multiparty video calls, are gaining a lot of popularity as they offer a rich communication experience. These applications have however, large requirements in terms of both network and computational resources and have to deal with sets of heterogenous clients. The multiparty videoconferencing systems can be grouped in two classes. They are based either on expensive central nodes, called multipoint control units (MCU), with transcoding capabilities, or, on a peer-to-peer strategy where users help each other to distribute the different video streams. Whereas the first one requires an expensive central hardware, the second one depends completely on the redistribution capacity of the users, which sometimes might neither provide sufficient bandwidth nor be reliable enough. In this work we propose an alternative solution where we use a central node to distribute the video streams but at the same time we maintain the hardware complexity and the computational requirements of this node as low as possible. The proposed solution uses a distributed algorithm to allocate the users' rates in a Quality of Service (QoS) aware manner. The allocation algorithm is also extremely fast and is able to quickly reallocate the rates in case the conditions change. We have further implemented our solution in a network simulator where we show that our rate allocation algorithm is able to properly optimize users' QoS and adapt to dynamic changes in the system. We also illustrate the benefits of our solution in terms network usage and average utility when compared to a baseline heuristic method operating on the same system architecture

Distributed Rate Allocation in Switch-Based Multiparty Videoconferencing System

Multiparty videoconferences, or more generally multiparty video calls, are gaining a lot of popularity as they offer a rich communication experience. These applications have, however, large requirements in terms of both network and computational resources and have to deal with sets of heterogenous clients. The multiparty videoconferencing systems are usually either based on expensive central nodes, called Multipoint Control Units (MCU), with transcoding capabilities, or on a peer-to-peer architecture where users cooperate to distribute more efficiently the different video streams. Whereas the first class of systems requires an expensive central hardware, the second one depends completely on the redistribution capacity of the users, which sometimes might neither provide sufficient bandwidth nor be reliable enough. In this work we propose an alternative solution where we use a central node to distribute the video streams, but at the same time we maintain the hardware complexity and the computational requirements of this node as low as possible, e.g. it has no video decoding capabilities. We formulate the rate allocation problem as an optimization problem that aims at maximizing the Quality of Service (QoS) of the videoconference. We propose two different distributed algorithms for solving the optimization problem: the first algorithm is able to find an approximate solution of the problem in a one-shot execution, whereas the second algorithm, based on Lagrangian relaxation, performs iterative updates of the optimization variables in order to gradually increase the value of the objective function. The two algorithms, though being disjointed, nicely complement each other. If executed in sequence, they allow to achieve both, a quick approximate rate reallocation in case of a sudden change of the system conditions, and a precise refinement of the variables which avoids problems caused by possible faulty approximate solutions. We have further implemented our solution in a network simulator where we show that our rate allocation algorithm is able to properly optimize users' QoS. We also illustrate the benefits of our solution in terms of network usage and overall utility when compared to a baseline heuristic method operating on the same system architecture

Reducing Internet Latency : A Survey of Techniques and their Merit

Bob Briscoe, Anna Brunstrom, Andreas Petlund, David Hayes, David Ros, Ing-Jyh Tsang, Stein Gjessing, Gorry Fairhurst, Carsten Griwodz, Michael WelzlPeer reviewedPreprin

Network utility maximization for delay-sensitive applications in unknown communication settings

In the last decades the Internet traffic has greatly evolved. The advent of new Internet services and applications has, in fact, led to a significant growth of the amount of data transmitted, as well as to a transformation of the data type. As a matter of fact, nowadays, the largest amount of traffic share consists of multimedia data, which do not represent classical Internet data. Due to the increasing amount of traffic, the network resources might be scarce, and in such cases it becomes extremely important to optimize network transmission in order to provide a satisfying service to the users. Although methods for maximizing the network utility in scenarios with limited resources have been studied extensively, the evolution of the Internet services poses continuously new challenges that require novel solution methods to meet the transmission requirements. In this thesis we propose novel solutions methods to network utility maximization problems that arise in the context of nowadays network communications. In particular we analyze problems related to delay-sensitive Internet applications and rate allocation in unknown network settings. In the first problem we study how to effectively allocate the transmission rates in a multiparty videoconference system. The main contribution of this chapter is an approximate fast rate rate allocation method that is able to adapt quickly to changes in the videoconference conditions. This fast adaptation cannot be achieved with classical network utility maximization solving methods, as they are usually based on iterative approaches. In this case we leverage the particular structure of the problem to design a novel distributed solving method which proves to be very effective when compared to baseline solutions. The next problem that we address is the design of a congestion control algorithm for delay-sensitive applications. One of the main problems of existing delay-based congestion control algorithms is that they tend to achieve an extremely low throughput when competing against loss-based algorithms. In order to overcome this difficulty we propose a novel adaptive controller based on a bandit problem approach. The adaptive controller tries to infer how the network responds, in terms of rate-delay pair at equilibrium, when changing the delay sensitivity of an underlying delay-based congestion control. Once the network response is inferred, the controller selects the sensitivity that leads to the best trade-off between the transmitting rate and the experienced delay. In the final problem, we analyze the design of an overlay rate allocation systems to be used when: the amount of available network resources is not known, and the user congestion feedback cannot be used as valid signal to reach the optimal rate allocation. Such a scenario appears when an Internet application wants to maximize a certain utility metric, but, at the same time, it must operate using a specific congestion control algorithm that is completely unaware of the application utility. To solve this problem we design a distributed system that coordinates the users in order to perform active learning on the amount of network resource. Adopting such a method reveals to be the key to an effective maximization of the long term application utility for the entire system

Coupled Congestion Control for RTP Media

Congestion occurs at a bottleneck along an Internet path; multiple flows between the same sender and receiver pairs can benefit from using only a single congestion control instance when they share the same bottleneck. These benefits include the ability to control the rate allocation between flows and reduced overall delay (multiple congestion control instances cause more queuing delay than one since each has no knowledge of the congestion episodes experienced by the others). We present a mechanism for coupling congestion control for real-time media and show its benefits by coupling multiple congestion controlled flows that share the same bottleneck