NOSSDAV'01, June 25-26, 2001, Port Jefferson, New York, USA.
 Copyright 2001 ACM 1-58113-370-7/01/0006... $5.00.

The Utility of Feedback in Layered Multicast Congestion Control

Sergey Gorinsky and Harrick Vin

Laboratory for Advanced Systems Research
Department of Computer Sciences
The University of Texas at Austin
Taylor Hall 2.124, Austin, TX 78712, USA,


Layered multicast is a common approach for dissemination of audio and video in heterogeneous network environments. Layered multicast schemes can be classified into two categories - feedback-based and feedback-free - depending on whether or not the scheme delivers feedback to the sender of the multicast session. Advocates of feedback-based schemes claim that feedback is necessary to match the heterogeneous receiver capabilities efficiently. Supporters of feedback-free schemes believe that feedback introduces significant complexity and that a moderate amount of additional layers can balance any benefit the feedback provides. Surprisingly, there has been no systematic evaluation of these claims. This paper provides a quantitative comparison of feedback-based and feedback-free layered multicast schemes with respect to aligning the provided service to the capabilities of heterogeneous receivers. We discover realistic scenarios when feedback-free schemes require a very large number of additional layers to match the performance of feedback-based schemes. Our studies also demonstrate that a light-weight feedback-based scheme can offer substantial improvement in performance over feedback-free schemes and can closely approximate the efficiency achieved by the optimal feedback-based scheme.

1.    Introduction

Layered multicast has been suggested as a solution for real-time dissemination of audio and video to heterogeneous receivers. In a layered scheme, the sender encodes media content into a stack of cumulative layers. The capability of a receiver determines which layers it can receive. Layered multicast schemes can be classified into two categories - feedback-based and feedback-free. Feedback-based schemes measure the receiver capabilities and communicate them to the sender. Based on this feedback, the sender adjusts the layer transmission rates to improve their alignment with the receiver capabilities. SAMM (Source-Adaptive Multi-layered Multicast) [16] and SIM (multicast congestion control with Selective participation, Intra-group transmission adjustment, and Menu adaptation) [6] are examples of feedback-based schemes. Feedback-free schemes deliver no feedback to the sender: the sender transmits the layers at predetermined constant rates; the receivers indicate to the network their desire to add or drop a layer by sending IGMP (Internet Group Management Protocol) join or leave messages [5], and, in response, routers modify their multicast routing tables using such protocols as DVMRP (Distance Vector Multicast Routing Protocol) [17]. Examples of feedback-free schemes include FLID (Fair Layered Increase/Decrease) [2], RLM (Receiver-driven Layered Multicast) [10], and RLC (Receiver-driven Layered Congestion control) [15]. While researchers have dedicated substantial efforts to the design of specific schemes, it is not established which approach - feedback-free or feedback-based - is preferable. As we discuss below, each approach has its own advantages and drawbacks. It has been shown that feedback-based schemes are better in aligning the transmission rates of layers to heterogeneous receiver capabilities. Unfortunately, to achieve this efficiency, these schemes introduce complexity of measuring and communicating the receiver capabilities to the sender. In particular, to resolve the problem of feedback implosion in large sessions, many feedback-based schemes use routers or designated servers to aggregate feedback. This infrastructure upgrade represents a detriment to the deployment of such schemes. Also, the precise measurement of all the receiver capabilities is difficult to realize in a heterogeneous multicast session. The common method of probing for capacities (increase the transmission if there is no congestion, decrease the transmission when congestion is detected) does not reveal all the receiver capabilities if the number of different capabilities exceeds the number of layers. In addition, since the feedback-based schemes adjust the transmission rates of the layers, the encoding process is more complex. The most appealing feature of feedback-free schemes is their simplicity: to control congestion, receivers transmit only IGMP messages. Since networks have to support this type of control traffic in a scalable manner anyway (for multicast routing), using IGMP messages for congestion control does not introduce additional complexity. On the other hand, feedback-free schemes can suffer from a significant mismatch between the statically allocated transmission rates and the changing capabilities of heterogeneous receivers. Advocates of feedback-free schemes argue that employing additional layers can reduce this mismatch. However, by increasing the number of layers, feedback-free schemes bring additional complexity into encoding at the sender and decoding at the receivers as well as raise the state and processing overhead in the routers (since these schemes use a separate multicast group for each layer). Besides, a larger number of layers affects adversely the compression efficiency, and this increases the bandwidth required to provide the same perceived quality. The above arguments suggest that if feedback-free schemes would need only a small number of additional layers to acquire the same level of efficiency as given by feedback-based schemes, feedback-free schemes would be preferable because of their simplicity. Surprisingly, the literature contains no quantitative assessment of such possibility. In this paper, we compare feedback-based and feedback-free schemes with respect to aligning the provided service to the capabilities of heterogeneous receivers. We quantify the comparison by measuring the additional number of layers required by feedback-free schemes to achieve a comparable alignment. Our studies reveal two realistic settings when this layer overhead of feedback-free schemes can be large: (1) the diversity of the receiver capabilities is smaller than the bandwidth range useful for the multicast content, and (2) the number of layers is roughly the same as the number of different receiver capabilities. These findings indicate tangible incentives for designing light-weight feedback-based schemes. We describe one such scheme that communicates only a small amount of information to the sender. Our experiments confirm that this light-weight feedback-based scheme can offer substantial improvement in performance over feedback-free schemes and can closely approximate the efficiency delivered by the optimal feedback-based scheme. The rest of the paper is organized as follows. Section 2 describes our model for bandwidth utility and receiver capabilities, performance metrics, and compared schemes. Section 3 explains the methodology, setup, and results of our experiments. Section 4 introduces and evaluates a light-weight feedback-based scheme. Section 5 summarizes our conclusions and presents directions for future research.

2.    Model

We consider a session where a single sender multicasts content using up to T cumulative layers. Let tk (such that k = 0, ..., T-1) denote the cumulative transmission rate for layer k. Similar to earlier studies of layered multicast [12], we represent these transmission rates tk with positive real numbers. We assume that layer 0 is the base layer of the hierarchical data encoding and that, for k = 1, ..., T-1, layer k refers to the k-th enhancement layer of the encoding. That is, we have 0 < t0 < t1 < ... < tT-1. The receivers of the session are characterized by their capabilities where the capability of a receiver is the maximum fair rate at which the receiver can receive data from the sender.

2.1    Bandwidth Utility

The International Telecommunication Union (ITU) provides guidelines for assessing the perceived quality of multimedia applications. For example, ITU-R recommendation BT.500 defines the video quality scale that ranges from 1 to 5: the value of 1 corresponds to a bad quality, 2 to poor, 3 to fair, 4 to good, and 5 to excellent [14]. Further, several studies have shown that while multimedia applications need certain minimal amount of bandwidth to be practicable, the marginal utility of additional bandwidth is negligible once the perceived quality becomes excellent [1]. Based on these studies, we represent the useful values of bandwidth as a possible range [l, vl] where l is the lowest possible capability (l > 0) and v is the possible heterogeneity (v1). We assume that bandwidth smaller than l has no utility for receivers and that bandwidth larger than vl does not increase the utility. For bandwidth b that is between l and vl, we characterize the bandwidth utility as a utility function u(b): when b = l, utility u(b) equals 1, i.e., the perceived quality is bad; as b grows to vl, utility u(b) increases to 5, i.e., to the excellent quality.

2.2    Receiver Capabilities

We assume that receiver capabilities lie within the possible range - receivers with capabilities below l do not benefit from receiving data from the session; capabilities that exceed vl are equivalent to vl in terms of their utility. Note though that the size l(v-1) of the possible range can be substantially larger than the actual span of receiver capabilities: e.g., when all the receivers share the same bottleneck link, their capabilities are identical. We characterize the actual span with two parameters, size h and shift z, that take their values between 0 and 1 (see Figure 1a):
(a) possible range and actual span (b) capabilities within the actual span

Figure 1: Characterizing the receiver capabilities (click here to see an enlarged version).

In reality, one could expect receiver capabilities to be clustered around particular values rather than spread uniformly throughout the actual span. There are two main reasons for such expectations. First, when receivers share a bottleneck link, their capabilities are the same. Second, if the bottleneck link of a receiver is its network access link, then the capability of this receiver is likely to be slightly below the bandwidth of a standard access technology such as 14.4 Kbaud, 28.8 Kbaud, 56 Kbaud modem or 144 Kbps, 192 Kbps, 384 Kbps, 768 Kbps, 1.1 Mbps, 1.5 Mbps DSL (Digital Subscriber Line). To model this clustered distribution of receiver capabilities, we assume that the multicast session has n receivers with C different receiver capabilities ci where Cn and i = 0, ..., C-1. We use ni to denote the number of receivers with capability ci (see Figure 1b).

2.3    Metrics

When evaluating the satisfaction of a receiver with the layered scheme, we consider only those layers that do not create congestion. We define an uncongested rate ri of a receiver with capability ci as:
For example, if each layer adds 1 Mbps to the total transmission rate, and the capability of a receiver is 1.25 Mbps, then the receiver can obtain (without causing congestion) only the base layer, and this gives the receiver an uncongested rate of 1 Mbps. Since the receiver cannot obtain the enhancement layers in their entirety, they are not considered. In this respect, the uncongested rate is similar to the ``goodput'' measure used in [16] to represent the quality of layered video. For a receiver with capability ci, we quantify its satisfaction with the transmission rates by defining a receiver dissatisfaction di as:
such that di0. Note that di = 0 when the layered scheme matches the receiver capability exactly. If the receiver does not receive even the base layer (i.e., ri = 0), then the dissatisfaction of this receiver is equal to the utility of its capability: di = u(ci). Since we define metric (2) as a difference of utilities, we can interpret the observed results in terms of the standard scale for the perceived quality. For instance, di = 1 means that the experienced quality is one level worse (such as poor instead of fair) than the receiver capability allows. To assess the overall satisfaction of the session with the layered scheme, we define a session dissatisfaction D as the average of the receiver dissatisfaction indices of all the receivers in the session:
Since feedback allows the sender to refine its estimates of the receiver capabilities, it is reasonable if a feedback-based scheme yields a lower session dissatisfaction than a feedback-free scheme with the same number of layers. The key question is how significantly do these session dissatisfactions differ. A good way to evaluate the significance of the difference is to measure how many additional layers a feedback-free scheme may need to have a comparable session dissatisfaction. We formally define this additional amount of layers as a layer overhead e:
where D0(T+g) is the session dissatisfaction for the feedback-free scheme with T+g layers, D1(T) denotes the session dissatisfaction for the feedback-based scheme with T layers, and s is a satisfaction similarity characterizing the closeness of the session dissatisfactions (s0; note that s = 0 when the feedback-free scheme has at most the same dissatisfaction as the feedback-based scheme).

2.4    Compared Schemes

The fundamental difference between feedback-based and feedback-free schemes is how much information about the receiver capabilities is available to the sender. Unlike a feedback-based scheme, a feedback-free scheme does not notify the sender about the actual capabilities of the receivers. Thus, the sender of the feedback-free scheme can rely only on a priori estimates of the capabilities to set the transmission rates. We assume that the feedback-free sender knows only the possible range [l, vl] of receiver capabilities and selects the transmission rates to cover it. In this paper, we examine two feedback-free schemes suggested in the literature [2,15]: Shacham [13] provides a dynamic programming algorithm that, given C, T, ci, and ni, computes an optimal scheme with respect to the session dissatisfaction. We refer to this scheme as an Optimal Layering (OL) scheme and use it as the (best possible) representative of feedback-based schemes.

3.    Experiments

3.1    Methodology

Studies of adaptive video and audio applications show that the marginal utility of bandwidth for such applications does not remain constant: when the signal quality begins to be viable, the marginal utility of extra bandwidth is substantial; as perceived quality improves, the marginal utility of additional bandwidth decreases [1,9]. However, evaluations of layered multicast schemes commonly employ metrics - such as the inter-receiver fairness [8] and reception granularity [3] - that assume that the satisfaction of a receiver grows linearly with the increase in the received rate. In our experiments, we examine both of these utility functions (see Figure 2):

Figure 2: Examined utility functions (click here to see an enlarged version).

We pick the values of receiver capabilities ci within the actual span randomly under the assumption of uniform distribution. Similarly, we select the values of ni randomly, under the assumption of uniform distribution, from interval [1, p] where p is the maximum number of receivers with the same capability. While we claim no special wisdom in instantiating our model for receiver capabilities, we believe that our representation is more realistic than those distributions that do not reflect the clustering of receiver capabilities.

3.2    Experimental Setup

In our experiments, we explore all the dimensions in the parameter space formed by our model: the maximum number of layers (parameter T), the bandwidth range useful for the multicast content (parameters l and v), the distribution of receiver capabilities (parameters z, h, C, and p), and the acceptable satisfaction similarity (parameter s). We observe how the choice of the bandwidth utility function (linear versus convex) and the layering pattern (additive versus multiplicative layering) affects the comparison of the feedback-based and feedback-free schemes. The default parameter settings in our experiments are as follows: T = 5 (the sender uses up to 5 layers), l = 1, v = 100 (the possible range is [1, 100], this can correspond to the range of video rates from 60 Kbps to 6 Mbps), z = 0.5, h = 0.5 (the actual span is in the middle of the possible range and covers half of it), C = 50 (there are 50 different capabilities), p = 399 (the number of receivers with a particular capability is picked randomly from interval [1,399]; thus, the expected number of receivers is (p+1)C/2=10000), and s = 0.05 (we measure how many additional layers a feedback-free scheme needs to brings its session dissatisfaction within 0.05 quality units from the session dissatisfaction for the OL scheme). When we vary a parameter, we consider a large number - 100 in most of the experiments - of its settings distributed uniformly throughout the examined range. For each considered setting, we generate 1000 session configurations (different due to the randomness in our experimental setup) and compute the session dissatisfactions for the OL, ML, and AL schemes as well as the layer overheads for the ML and AL schemes. We present the results graphically as lines connecting the points that correspond to the averages, over all the generated configurations, of the computed values.

3.3    Results

3.3.1    The Dependence on the Capability Distribution

First, we examine how the location of the receiver capabilities within the possible range impacts the performance of the OL, AL, and ML schemes. Figure 3a shows that when the receiver capabilities are homogeneous (i.e., for low h), OL provides an almost perfect satisfaction while AL and ML need up to 60 additional layers to supply a comparable level of efficiency. As h increases, the growing diversity of the receiver capabilities drives the efficiency of OL down, and the layer overheads of AL and ML converge to about 10 layers or below.

Figure 3: The dependence on the: (a) actual span size, (b) actual span shift, and (c) number of different capabilities (click here to see an enlarged version).

A crucial observation for understanding the dependency on the utility function is that for different functions, the same distribution of the receiver capabilities covers different spans on the utility scale. Since z = 0.5 in the discussed experiment, the actual span lies in the middle of the possible range. Hence, when the actual span is small in comparison to the possible range, the utility diversity of the actual span is smaller for the convex utility (see Figure 2). Consequently, all the examined schemes achieve better performance with the convex utility for low h. When h is high, the utility diversities are similar for the considered utility functions, and the placement of the transmission rates becomes a more influential factor. Since the convex function has much larger marginal utility at low capabilities, it gives much greater dissatisfaction for the same difference in bandwidth in the vicinity of l. For AL with the convex utility, the higher dissatisfaction of less capable receivers substantially outweighs the lower dissatisfaction of more capable receivers. Consequently, AL performs better with the linear utility when h is large. In comparison to AL, the ML scheme places its rates closer to l, and this allows ML to maintain better performance with the convex utility throughout the possible range. With increase of the actual span shift z (see Figure 3b), the performance balance between the feedback-free schemes flips: since AL, in comparison to ML, sets its transmission rates further from l, the ML scheme outperforms AL if z is low (i.e., when the receiver capabilities are closer to l); for larger z, AL provides lower dissatisfaction than ML. A similar flipping pattern characterizes the dependence on the utility function. Each of the three schemes achieves better performance with the linear utility for low z and with the convex utility for high z. Once again, the reason for such behavior lies with the utility diversity of the receiver capabilities: while the linear function yields a smaller diversity when z is low, the convex function provides a smaller diversity when z is large.

Figure 4: The dependence on the number of layers (click here to see an enlarged version).

Figure 5: The dependence on the: (a) possible heterogeneity and (b) lowest possible capability (click here to see an enlarged version).

In Figure 3c, we vary C, the number of different capabilities, while keeping the expected number of receivers close to 10000. Because changes in C do not alter the utility diversities of the actual span, the convex utility - which furnishes a smaller utility diversity for the examined actual span - gives lower dissatisfaction to all the schemes and lower layer overhead to both feedback-free schemes. When the number of different capabilities is at most the number of layers, OL yields the 100% satisfaction. For larger values of C, the session dissatisfaction for OL ascends while the session dissatisfactions for AL and ML remain on higher but relatively constant levels. Due to the declining efficiency of OL, the layer overhead for the feedback-free schemes - which can be as large as 40 layers when the number of layers and the number of different capabilities are roughly the same - reduces as the number of capabilities grows. In contrast, as we observed by varying p while keeping C fixed (we omit the corresponding graphs due to space constraints), the number of receivers makes virtually no impact on the performance of the OL, AL, and ML schemes.

3.3.2    The Dependence on the Number of Layers

Figure 4 shows that as T, the number of layers, grows, ML and AL fail to reach the same satisfaction levels as provided by OL. Moreover, as T increases up to 10 layers, the feedback-free schemes incur greater layer overheads to provide comparable session dissatisfactions. On the other hand, less than 10 layers enable OL to bring the session dissatisfaction close to 0. Since the utility diversity of the examined actual span is lower with the convex function, the convex utility provides ML and AL with notably smaller dissatisfactions and layer overheads for all but low values of T. When T=2 or T=3, receivers that can receive only the base layer rate of l are abundant. Because the dissatisfaction of such receivers is much greater with the convex function, the linear utility provides the feedback-free schemes with better average performance when T is so low.

3.3.3    The Dependence on the Possible Range

Figure 5a displays that while the possible heterogeneity v does not change the performance of the OL and AL schemes, the session dissatisfaction and layer overhead of ML grow as v increases. For the largest examined value v = 500, the layer overheads of ML for the convex utility and linear utility become around 22 and 26 layers respectively. However, the layer overhead of AL remains at about 6 layers for both utility functions. The difference in the behaviors of the AL and ML schemes can be explained as follows. When the possible range increases, the actual span also expands so that to stay in the middle of the possible range and cover half of it (since z and h remain equal to 0.5). The transmission rates of AL also increase proportionally to spread uniformly throughout the new possible range. As a result, the relative positions of the actual span and the transmission rates of AL do not change. Consequently, the increase in v does not affect the performance of the AL scheme. On the other hand, as v grows, the transmission rates of ML shift towards l with respect to the increasing actual span. In particular, more layers in ML have cumulative transmission rates that are below the smallest receiver capability, and also the number of the ML transmission rates within the actual span decreases. Thus, the performance of the ML scheme degrades as the possible heterogeneity increases. Figure 5b indicates that the lowest possible capability l does not affect the performance of OL, AL, and ML. This shows that the units of capability measurements are irrelevant to the comparison of the feedback-based and feedback-free schemes.

3.3.4    The Impact of the Satisfaction Similarity

Figure 6 studies the dependence of the layer overheads on the satisfaction similarity s. To bring the average session dissatisfaction within 0.2 quality units - which constitute a substantial difference for audio and video perception - from the dissatisfaction of the OL scheme, AL needs 4 additional layers with the linear utility (or 2 additional layers with the convex utility) while ML needs 10 and 5 additional layers with the linear utility and convex utility respectively. As s decreases, the layer overheads grow exponentially. To make their session dissatisfactions differ from the level of the OL scheme by 0.01 quality units, the AL and ML schemes require more than 58 and 111 additional layers respectively. Once again, because the utility diversity of the examined receiver capabilities is lower with the convex function, the convex utility provides AL and ML with lower layer overheads for all values of s.

3.3.5    Summary

This section compared the feedback-based OL scheme with the feedback-free AL and ML schemes along all the dimensions in the parameter space formed by our model. The distribution of receiver capabilities was shown to affect the performance substantially. We identified two major scenarios where OL is significantly superior to the feedback-free schemes: (1) the receiver capabilities are homogeneous in comparison to the bandwidth range useful for the multicast content, and (2) the number of layers and the number of different capabilities in the session are on the same order of magnitude. While the number of different capabilities is an important parameter, the impact of the session size is negligible. Similarly, the location and size of the possible range do not appear to influence the performance of the examined schemes (except ML). Somewhat surprisingly, additive layering consistently outperforms multiplicative layering. The only exception takes place when the receiver capabilities cluster closer to the bottom of the possible range (i.e., for low z). With respect to the bandwidth utility functions, the convex utility commonly provides lower session dissatisfaction except in the cases of low z (for all the schemes), extra low T (for the feedback-free schemes), and high h (for the AL scheme).

Figure 6: The impact of the satisfaction similarity (click here to see an enlarged version).

4.    Utility of Limited Feedback

Our findings reveal two realistic scenarios when layered multicast can greatly benefit from information about the distribution of the receiver capabilities: (1) the span of the capabilities is much smaller than the bandwidth range useful for the multicast content, and (2) the number of layers and the number of different capabilities in the session are roughly the same. This indicates tangible incentives for designing light-weight feedback-based schemes: if the sender of a homogeneous session would learn the actual span of the capabilities (a much easier task than discovering all the capabilities), the session would need a much smaller number of layers to achieve high satisfaction than if the sender knew only their possible range. Thus, a successful approach can be a hybrid of the feedback-free and feedback-based paradigms: feedback informs - possibly at a coarse timescale - the sender about the lowest and highest capabilities in the session, and the sender uses this range to set - in the feedback-free fashion - the transmission rates of the layers. Below, we consider a Hybrid Layering scheme (HL) that communicates the smallest receiver capability c0 and the highest receiver capability cC-1 to the sender of the session. Using this information, the sender sets the cumulative transmission rates tk of the HL scheme so that they grow additively by (cC-1 - c0) / T per layer:
While the scalable communication of c0 and cC-1 to the sender is not a straightforward task (see [11] for a possible approach), the sender in HL needs to obtain only these two pieces of information regardless of the session size. Thus, we conjecture that there is no fundamental reason why the HL scheme could cause feedback implosion and consequently should require feedback aggregation. In what follows, we compare the performance of HL with the already examined OL, AL, and ML schemes in the two major scenarios when the feedback-free schemes incur substantial layer overhead. To assess the performance of HL in the case of homogeneous receiver capabilities, we repeat the experiment from Figure 3a for the OL, AL, ML, and HL schemes. Figure 7a shows that HL offers a dramatic improvement in dissatisfaction and layer overhead over the feedback-free schemes when h is small, i.e., when the feedback-free schemes are the most inefficient. Moreover, the session dissatisfaction of HL remains close to the level provided by the optimal scheme OL. Consequently, the layer overhead of this hybrid scheme is low: for small values of h, the layer overhead of HL is negligible; as the receiver capabilities spread to cover the possible range (when h =1), the overhead increases but still stays below 3 layers. Hence, HL extracts a great benefit from the limited feedback of just two values c0 and cC-1. The utility of communicating the rest of the receiver capabilities to the sender is significantly lower. According to the comparison of the feedback-free schemes in Section 3, additive layering consistently outperforms multiplicative layering except when z is small. Since the examined HL scheme sets its transmission rates in the additive fashion, it is interesting to check whether this feature undermines the efficiency of HL when z is low. Figure 7b repeats the experiment from Figure 7a for z=0.1 and demonstrates that HL preserves its superior performance in comparison to both the AL and ML schemes. Furthermore, the HL scheme still needs only a few of additional layers to make its session dissatisfaction comparable to the one provided by the optimal feedback-based scheme. To evaluate the performance of HL in the second major disadvantageous case for the feedback-free schemes (i.e., when the number of layers and the number of different capabilities are roughly the same), we repeat the experiment from Figure 3c with the OL, AL, ML, and HL schemes and report the results in Figure 7c. Once again, HL performs much better than the AL and ML schemes. For the linear utility function, the peak layer overheads for the ML, AL, and HL schemes are 44, 21, and 7 additional layers respectively. For the convex utility, the maximum overheads for ML, AL, and HL are 27, 12, and 2 layers respectively. While the HL scheme greatly outperforms the feedback-free schemes, Figure 7c indicates opportunities for further improvement. It is possible that different light-weight schemes with a small amount of feedback information (such as the top T most common receiver capabilities inferred from the multicast tree topology [7]) can provide even better performance.

Figure 7: The assessment of the hybrid scheme HL (click here to see an enlarged version).

5.    Concluding Remarks

This paper compared feedback-free and feedback-based layered multicast schemes with respect to aligning the provided service to the capabilities of heterogeneous receivers. We discovered several realistic scenarios when feedback-free schemes require a very large number of additional layers to match the performance of feedback-based schemes. We also demonstrated that a light-weight feedback-based scheme can offer substantial improvement in performance over feedback-free schemes and can closely approximate the efficiency delivered by the optimal feedback-based scheme. We envision two main directions for future work. Section 5.1 discusses how to make our current model more realistic. Sections 5.2 and 5.3 outline two aspects of layered multicast that are, in our opinion, important but routinely ignored by proposed multicast designs.

5.1    Model Refinements

To compare the feedback-based and feedback-free schemes, we used the layer overhead as a chief metric. However, our current model does not reflect two factors that can substantially increase the number of additional layers needed by a feedback-free scheme to compensate for the lack of feedback. First, a larger number of layers usually leads to a smaller compression ratio. Accounting for the declining compression ratio not only can raise the layer overhead but also is likely to reveal scenarios when additional layers cease to improve satisfaction (i.e., when the layer overhead is infinite). Another relevant issue with feedback-free congestion control is its responsiveness: after the last subscriber of a session in a subnet sends an IGMP leave message, up to 10 seconds can elapse before the last-hop router stops forwarding the session traffic into the subnet. The feedback-free technique of dynamic layering [2] tackles this problem of responsiveness but at the price of even further increase in the number of additional layers. We plan to adjust our model to represent the dependencies on the compression ratio and control responsiveness.

5.2    The First-Hop Problem

To control congestion, feedback-free schemes adjust multicast routing tables. Therefore, a feedback-free scheme cannot resolve congestion if it occurs on the first hop between the sender and the top router in the multicast tree of the session. The sender keeps transmitting all the layers into the network regardless of the congestion and the receiver capabilities. Even if the session has no subscribed receivers, the sender can keep sending its packets which are discarded by the first router. This first-hop problem can manifest itself as unfair sharing of bandwidth or even as congestion collapse. In future, we will assess the severity of the first-hop problem and work on possible solutions. It appears that to be safe for deployment in the Internet, feedback-free layered multicast protocols should be made end-to-end by propagating the layer subscription information all the way to the sender. This approach, however, requires changes in the network infrastructure and brings additional complexity.

5.3    Trust and Privacy

While it has been argued that multicast congestion control should not assume trust and cooperation among receivers [4], existing congestion control designs for multicast commonly ignore the issues of distrust and privacy. Since receivers are the only entities regulating congestion in feedback-free schemes, these schemes seem to be more vulnerable in the presence of misbehaving receivers. Including the sender into the congestion control loop can protect against the threats posed by some types of receiver misbehavior. We are investigating whether the feedback-based approach to layered multicast can ensure better performance in a distrusted network environment.

6.    Bibliography

L. Breslau and S. Shenker. Best-Effort versus Reservations: A Simple Comparative Analysis. In Proceedings ACM SIGCOMM'98, September 1998.

J. Byers, M. Frumin, G. Horn, M. Luby, M. Mitzenmacher, A. Roetter, and W. Shaver. FLID-DL: Congestion Control for Layered Multicast. In Proceedings NGC 2000, November 2000.

J. Byers, M. Luby, and M. Mitzenmacher. Fine-Grained Layered Multicast. In Proceedings IEEE INFOCOM 2001, April 2001.

N.G. Duffield, M. Grossglauser, and K.K. Ramakrishnan. Distrust and Privacy: Axioms for Multicast Congestion Control. In Proceedings NOSSDAV'99, June 1999.

W. Fenner. Internet Group Management Protocol, Version 2. RFC 2236, November 1997.

S. Gorinsky, K.K. Ramakrishnan, and H. Vin. Addressing Heterogeneity and Scalability in Layered Multicast Congestion Control. Technical Report TR2000-31, Department of Computer Sciences, The University of Texas at Austin, November 2000.

S. Jagannathan, K. Almeroth, and A. Acharya. Topology Sensitive Congestion Control for Real-Time Multicast. In Proceedings NOSSDAV 2000, June 2000.

T. Jiang, E. W. Zegura, and M. H. Ammar. Inter-Receiver Fairness: A Novel Performance Measure for Multicast ABR Sessions. In Proceedings ACM SIGMETRICS'98, June 1998.

J. Kimura, F.A. Tobagi, J. Pulido, and P.J. Emstad. Perceived Quality and Bandwidth Characterization of Layered MPEG-2 Video Encoding. In Proceedings of the SPIE International Symposium on Voice, Video and Data Communications, September 1999.

S. McCanne, V. Jacobson, and M. Vetterli. Receiver-driven Layered Multicast. In Proceedings ACM SIGCOMM'96, August 1996.

L. Rizzo. pgmcc: A TCP-friendly Single-Rate Multicast Congestion Control Scheme. In Proceedings ACM SIGCOMM 2000, August 2000.

D. Rubenstein, J. Kurose, and D. Towsley. The Impact of Multicast Layering on Network Fairness. In Proceedings ACM SIGCOMM'99, September 1999.

N. Shacham. Multipoint Communication by Hierarchically Encoded Data. In Proceedings IEEE INFOCOM'92, May 1992.

International Telecommunication Union. Methodology for the Subjective Assessment of the Quality of Television Pictures. ITU-R Recommendation BT.500-10, March 2000.

L. Vicisano, L. Rizzo, and J. Crowcroft. TCP-like Congestion Control for Layered Multicast Data Transfer. In Proceedings IEEE INFOCOM'98, March 1998.

B. Vickers, C. Albuquerque, and T. Suda. Source-Adaptive Multi-Layered Multicast Algorithms for Real-Time Video Distribution. IEEE/ACM Transactions on Networking, December 2000.

D. Waitzman, C. Partridge, and S. Deering. Distance Vector Multicast Routing Protocol. RFC 1075, November 1988.

Sergey Gorinsky