Using multicast delivery to multiple receivers reduces the aggregate bandwidth required from the network compared to using unicast delivery to each receiver. However, multicast is not yet widely deployed in the Internet. One reason is the lack of incentive to use multicast delivery. To encourage the use of multicast delivery, we define a new bandwidth-allocation policy, called LogRD, taking into account the number of downstream receivers. This policy gives more bandwidth to a multicast flow as compared to a unicast flow that shares the same bottleneck, without starving the unicast flows, however. The LogRD policy also provides an answer to the question on how to treat a multicast flow compared to a unicast flow sharing the same bottleneck. We investigate three bandwidth-allocation policies for multicast flows and evaluate their impact on both receiver satisfaction and fairness using a simple analytical study and a comprehensive set of simulations. The policy that allocates the available bandwidth as a logarithmic function of the number of receivers downstream of the bottleneck achieves the best tradeoff between receiver satisfaction and fairness.

We investigate the estimation of the size of a broadcast audience. We give the maximum likelihood (ML) estimators for different scenarios. Using a Poisson approximation we obtain upper and lower bounds for the audience size. The ML estimators has closed-form expressions, so do the bounds. We treat the problem of a malfunctioning feedback flow control, where the sender is overwhelmed by feedback messages. The ML estimate of the audience size is obtained using the expectation-maximization (EM) algorithm, even under such conditions as censored values due to feedback implosion. Quantifying the influence of feedback implosion on the loss of information for the audience estimation, we further investigate the choice of the parameters in order to optimize estimation results.

We examine the impact of the loss recovery mechanism on the performance of a reliable multicast protocol. Approaches for loss recovery in reliable multicast can be divided into two major classes: centralized (source-based) recovery and distributed recovery. For both classes we consider the state of the art: for centralized recovery, an integrated transport layer scheme using parity multicast for error recovery (hybrid ARQ type 2) as well as timer-based feedback suppression, and for distributed recovery, a scheme with local data multicast retransmission and feedback processing in a local neighborhood. We also evaluate the benefits of combining the two approaches into distributed error recovery (DER) with local retransmissions using a type 2 hybrid ARQ scheme. The schemes are evaluated for up to 10/sup 6/ receivers under different loss scenarios with respect to network bandwidth usage and completion time of a reliable transfer. We show that using DER with type 2 hybrid ARQ gives best performance in terms of bandwidth and latency. For networks, where local retransmission is not possible, we show that a centralized protocol based on type 2 hybrid ARQ comes close to the performance of a protocol with local retransmissions.

Using multicast delivery to multiple receivers reduces the aggregate bandwidth required from the network compared to using unicast delivery to each receiver. To encourage the use of multicast delivery, a higher amount of bandwidth should be allocated to a multicast row as compared to a unicast row that share the same bottleneck, but without starving the unicast flow. We investigate three bandwidth allocation policies for multicast flows and evaluate their impact on the bandwidth received by the individual receivers. The policy that allocates the available bandwidth as a logarithmic function of the number of receivers downstream of the bottleneck achieves the best trade-off between maximizing the receiver satisfaction and keeping fairness high.

We investigate the scalability of feedback in multicast communication and propose a new method of probabilistic feedback based on exponentially distributed timers. By analysis and simulation for up to 10/sup 6/ receivers, we show that feedback implosion is avoided while feedback latency is low. The mechanism is robust against the loss of feedback messages and works well in case of homogeneous and heterogeneous delays. We apply the feedback mechanism to reliable multicast and compare it to existing timer-based feedback schemes. Our mechanism achieves lower negative acknowledgment character (NAK) latency for the same performance in terms of NAK suppression. No topological information of the network is used, and data delivery is the only support required from the network. The mechanism adapts to a dynamic number of receivers and leads to a stable performance for implosion avoidance and feedback latency.

We investigate the scalability of feedback in multicast communication and propose a new method of probabilistic feedback based on exponentially distributed timers. By analysis and simulation for up to 10/sup 6/ receivers we show that feedback implosion is avoided while feedback latency is low. The mechanism is robust against the loss of feedback messages and robust against homogeneous and heterogeneous delays. We apply the feedback mechanism to reliable multicasting and compare it to existing timer-based feedback schemes. Our mechanism achieves lower NAK (loss signaling) latency for the same performance in terms of NAK suppression. It is scalable, the amount of state at every group member is independent of the number of receivers. No topological information of the network is used and data delivery is the only support required from the network. It adapts to the number of receivers and leads therefore to a constant performance for implosion avoidance and feedback latency.

We examine the impact of the loss recovery mechanisms on the performance of a reliable multicast protocol. Approaches to reliable multicast can be divided into two major classes: source-based recovery, and distributed recovery. For both classes we consider the state of the art: for source-based recovery, a type 2 hybrid ARQ scheme with parity retransmission; for distributed recovery, a scheme with local multicast retransmission and local feedback processing. We further show the benefits of combining the two approaches and consider a type 2 hybrid ARQ scheme with local retransmission. The schemes are compared for up to 10/sup 6/ receivers under different loss scenarios with respect to network bandwidth usage and completion time of a reliable transfer. We show that the protocol based on local retransmissions via type 2 hybrid ARQ performs best for bandwidth and latency. For networks, where local retransmission is not possible, we show that a protocol based on type 2 hybrid ARQ comes close to the performance of a protocol with local retransmissions.

We investigate how forward error correction (FEC) can be combined with automatic repeat request (ARQ) to achieve scalable reliable multicast transmission. We consider the two scenarios where FEC is introduced as a transparent layer underneath a reliable multicast layer that uses ARQ, and where FEC and ARQ are both integrated into a single layer that uses the retransmission of parity data to recover from the loss of original data packets. To evaluate the performance improvements due to FEC, we consider different loss rates and different types of loss behavior (spatially or temporally correlated loss, homogeneous or heterogeneous loss) for up to 10/sup 6/ receivers. Our results show that introducing FEC as a transparent layer below ARQ can improve multicast transmission efficiency and scalability. However, there are substantial additional improvements when FEC and ARQ are integrated.

Our aim is to investigate reliable transmission for multicast communication and explore its relationship to multicast routing. We derive two characterizations that enable the comparison of routing algorithms and error recovery mechanisms with respect to the multicast tree topology, namely the probability mass function of successful receptions and the expected number of retransmissions needed to deliver a packet from the source to all receivers. We also give a tight approximation of the computationally expensive expected number of retransmissions. These expressions allow to explore the relationship between routing and error recovery for multicast communication. We finally evaluate the impact of routing algorithms on the performance of reliable multicast transmission and give a realistic generic model for a multicast tree.

We investigate how FEC (Forward Error Correction) can be combined with ARQ (Automatic Repeat Request) to achieve scalable reliable multicast transmission. We consider the two scenarios where FEC is introduced as a transparent layer underneath a reliable multicast layer that uses ARQ, and where FEC and ARQ are both integrated into a single layer that uses the retransmission of parity data to recover from the loss of original data packets.To evaluate the performance improvements due to FEC, we consider different types of loss behaviors (spatially or temporally correlated loss, homogeneous or heterogeneous loss) and loss rates for up to 106 receivers. Our results show that introducing FEC as a layer below ARQ can improve multicast transmission efficiency and scalability and that there are substantial additional improvements when the two are integrated.