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On Interaction between Loss Characterization and Forward Error Correction in Wireless Multimedia Communication Abdelhamid Nafaa, Yassine Hadjadj-Aoul and Ahmed Mehaoua University of Versailles – CNRS-PRiSM Lab., 45 avenue des Etats-Unis, 78035 Versailles — France {anaf, yana, mea}@prism.uvsq.fr

Abstract—With the steadily growing synergy between existing heterogeneous networks, the wireless LAN appears as the de-facto wireless access network in the end-to-end multimedia services distribution chain. Unlike in the traditional wired multi-hop networks (Internet) where congestions increase persistently both delays and losses, wireless packet losses are often location- and timevarying. Particularly, WLAN communication is characterized by high bit error rates that translates into tight loss dependency. The loss process may rapidly shift between different loss correlations levels, resulting in poor forward error correction (FEC) recovery capabilities. In this paper, we address this issue by providing a combined loss model to accurately characterize the wireless loss distribution features. We use control theory guided parameter tuning in order to urge the convergence of the loss models towards seizing the instantaneous loss distribution trends. Finally, we derive a new loss-specific QoS metrics for new FEC block allocation scheme.

I. INTRODUCTION Among Quality of Service (QoS) metrics, packet loss is an important metric in shared environments such as the WLAN’s [3]. While existing works focus on capturing the mean loss (long-term QoS), less emphasis is put on modeling loss distribution (short-term QoS, cf. [4] [5] and the references therein). In certain real-time applications, the loss pattern is a key parameter that determines the performance observed by the users. For the same loss rate, two different loss distributions could potentially produce widely different perceptions of performance [6]. Also, many forward error recovery approaches become less efficient as the loss burstiness increases. It is, therefore, essential for streaming application to capture and quantify the loss process with suitable QoS metrics in order to improve the efficiency of QoS adaptive mechanisms. The intrinsic wireless link characteristics involve unpredictable burst errors that are usually uncorrelated with the instantaneous available bandwidth; this often translates into sporadic and clustered packet losses. One particular issue to tackle when streaming media is the FEC efficiency/accuracy. In this work, we investigate video multicast communications over IEEE 802.11b wireless LAN. Our target environment is a video server that multicasts video streams for a large group of clients. In our large-scale network architecture, we transmit a single multicast stream together with different FEC streams tailored for different operated WLANs that are connected to the IP backbone through a broadband metropolitan DVB-T network; the DVB-T network ensures, among other things, filtering of FEC streams by transmitting each FEC stream to the appropriate WLAN. Within a given WLAN, the FEC stream is used at different receivers to recover, to some extents, possible network losses. Each WLAN may subscribe

to a broad range of QoS levels (Service Level Agreement — SLA), which correspond to different adaptive FEC responsiveness levels. We believe that this scenario reasonably represents many of current [2] and forthcoming streaming services. The emphasis is put on the WLAN since it is actually considered as the “de-facto” wireless access network in many Service Providers offers [1]. In this work, we focus on better understanding the WLAN’s “burstiness” effect in order to characterize the channel behavior with more accurate QoS metrics. As pointed out in this work, the loss process in wireless channel shows certain “stationary”1 behavior over the time [8]. This comes out through a significant stability in the successive measured loss run lengths distribution. Capturing the loss distribution trends may be useful, in many manners, for multimedia applications adaptation, e.g., allocating the optimal FEC block, or even applying a reasonable interleaved FEC protection [6]. To tackle the wireless loss uncertainties, we combine a loss run length model and an inter-loss distance model to accurately capture the channel burstiness and the clustering between loss runs. This combined loss model relies on an accurate network feedback (extended RTCP) that indicates for each transmitted packet whether it was lost or received. We use a simple fuzzy controller to instantaneously refresh our combined loss model, and then anticipate wireless channel fluctuations taking into account the knowledge acquired from past experience. This achieved by canceling/minimizing each time the possible model approximations, thereby urging the convergence of our models towards capturing the current loss correlation features. Finally, we use the latter prediction, together with new loss-specific metrics, to effectively protect multimedia streams using a new bandwidthefficient FEC allocation scheme. The remainder of this paper is as follows. Section II investigates reliable video multicasting over Wireless LAN. Section III describes our proposal: a combined wireless loss characterization and channel coding. Section IV is devoted to our models validation as well as to the definition of an adaptive fuzzy-based channel behavior prediction. The performance evaluation of our streaming system is presented in Section V. Finally, we have drawn several key conclusions from this work; these are stated in Section VI.

II. BACKGROUND AND MOTIVATIONS Typical communications over WLAN involve a high bit error, which usually occurs through correlated adjacent packets losses. An understanding of the effect of packet loss on the reconstructed video quality is clearly very important for designing and operating video 1

Although the term “stationary” typically refers to stochastic processes that do not change behavior over time, we use it in this paper to exclusively point out a cyclic dependency between loss distribution measures.

communication systems over error-prone networks. In case of multicast or broadcast communication over WLAN, the data packets are not acknowledged, and hence no retransmission is performed at the MAC/Logical link layer; this mode of communication reduces transmission delays and data control overheads while making communications less reliable. Another important implication, with multicast communication in WLAN’s, is that the AP’s is unable to change its nominal bit rate (11, 5.5, 2, or 1 Mbps) as the communication experience an increased BER [7]. Usually, in directed communication, both the wireless station and the Access Point may explicitly degrade their nominal bit rate by altering both coding and physical modulation in order to overcome a channel’s noisy period (i.e., after repeated unsuccessful frame transmissions). Even though the AP may be enhanced to sense the receivers’ traffic in order to degrade its nominal bit rate accordingly, the period during which the receivers experience severe degradations may last longer time periods. In this context, the transient time-varying harsh conditions may steadily persist over the time. Generally, reliable transmission on wireless channels requires the use of some type of error control. Efficient error control on timevarying channels can be performed by implementing an adaptive control system where the optimum code is selected according to the actual channel conditions. In [8], the author proposes track the short time intervals where the channel parameters are stable enough so as to improve the recovery efficiency. Authors in [9], propose an adaptive FEC based on redundant audio samples transmission (audio packets), so the information relative to packet n may be spread over multiple packets relying on a simple two-states Gilbert model to react to network fluctuations. This scheme is very efficient for Internet telephony application since a Gilbert model suffice to capture the quite “soft” loss process dynamics involved by the persistent congestion of traditional multi-hop IP networks. Clearly, it is important to capture the wireless channel dynamics through pertinent QoS metrics to figure out the appropriate FEC scheme that maximizes recovery effectiveness.

III. A COMBINED LOSS CHARACTERIZATION AND CHANNEL CODING 1.

Note that a model can be entirely described by its burst loss length occurrences vector M (i.e. the mi coefficient vector, M = (m0, m1, mn-1 1) . The formula to calculate the parameters of the extended Gilbert model is given by: n −1

P(X ≥ k) P ( k − 1 )( k ) = P ( X ≥ k X ≥ k − 1 ) = = P ( X ≥ k − 1)

P(n-1)0 = 1 - P(n-1)(n-1) = 1

P20

∑

mi

At this point, the mean burst loss length (MBL) is easily deduced: ( MBL

n −1

∑

i ⋅ m i)

i =1 n −1

=

∑

(1)

mi

i =1

MBL gives the expected mean loss run length based on the previously observed loss distribution.

2.

Inter-loss Distance Model

The inter-loss distance metric was recently proposed to describe the distance between packet losses in terms of sequence number. The ILD (Inter-Loss Distance) metric is useful in two respects. First, an accurate loss model is able to model loss run distributions, but it does not model distances between loss runs. Second, small ILDs may also degrade the performance of FEC codes. As with the loss model, we derive a model to characterize the ILD’s distribution features. This is useful to understand and foretell the spacing between loss events. Let di, i=1,.. n-1 denote the number of ILDs having length i. The ILD model is completely described by its ILD’s occurrence vector given by: D=(d1, d2, …., dn-1)-1. The mean inter-loss distance (MILD) is given by: ( MILD

=

n −1

∑

i ⋅ d i)

i=1 n −1

∑

(2)

di

i =1

The inter-loss distance metric allows one to study the separation between packet losses. This is particularly useful to complement the loss model for an enhanced loss pattern prediction and multimedia application adaptation.

Efficient FEC Protocol for Video Distribution

Packet level FEC consists of producing h redundant packets from k original ones, providing a resiliency against a maximum of h losses out of n packets constituting the FEC block (n=h+k ). In order to reflect at application level the wireless channel dynamics, we use both MBL and MILD as parameters in FEC blocks. Using an appropriate amount of redundancy h = MBL makes communications robust against the most likely expected clustered losses. On the other hand, we take as the number of original data k = MILD in order to maximize the number of protected media packets (see Fig. 3). At this point, the FEC block is constituted of n=MBL+MILD packets. The above described FEC block allocation scheme is efficient as long as the wireless channel still exhibits stable behavior over the time. FEC

P00 = 1 - P01

i= k n −1

i= k −1

3.

Loss Run Length Model

In this section we present an extension of the well-known Gilbert model. The extended Gilbert model was previously used in modeling Internet losses. It allows treating the issue of long loss runs by using models with multiple states. We define the random variable X as follows: X = 0: “non packet loss”, X = k: “exactly k consecutive packets lost”, and X ≥ k: “at least k consecutive packets lost”. With this definition, we establish a loss run-length (loss burst length) model with n states (see Figure 1). We rely on an extended RTCP feedback that continuously reports on the measured loss pattern. Each RTCP report corresponds to the last transmitted 300 packets (i.e., loss pattern segment).

( ∑ mi )

FEC

Burst error (MBL)

P(n-1)(n-1)

FEC Packets

P10 S1

S0

S2

(1 loss)

(non loss)

P01

(2 losses)

P12

....

S n-1 (n-1 losses)

P(n-2)(n-1)

Fig. 2. Extended Gilbert model with limited states.

The system keeps a counter l, which is the number of consecutively lost packets; it is reset whenever the next packet is delivered. The parameter to determine is P[Xi | Xi-1 to Xi-l all lost]. Let mi, i = 1, 2... n-1 denote the number of loss bursts having length i, where n – 1 is the longest loss bursts. m0 denotes the number of delivered packets. n represents also the number of model’s states.

RTP media Packets

MILD

Fig. 3. Model-based FEC block allocation scheme.

Since we are restricted by delay constraints, choosing a large k > MILD may lead to intra-media synchronization and timeouts problems. In realistic streaming systems it is, indeed, much suitable to apply FEC to no more than one frame (see [6]). Consequently, we generalize the previous analysis in order to apply our redundancy allocation scheme to any block size (k). In our case, k stands for the

number of packets constituting the current video Frame to be transmitted. The amount of FEC redundancy h for k original video packets is then obtained as follows: h=

k ⋅ MBL MILD

(3)

IV. ADAPTIVE MODEL-BASED LOSS CHARACTERIZATION In this section, we aim at better predicting the wireless loss pattern, focusing on burst loss distribution. One important issue that should be overcame, when streaming media with FEC protection, is to capture the dependency between losses. Especially, both burst loss length and inter-losses distance (ILD) have devastating consequences on FEC efficiency, and thus the video quality at receiver. In this context, it is much suitable to use FEC in order to evaluate a particular loss model and its capability to predict the short-term loss features (burst loss length and ILD); this consists in comparing the experimented loss pattern and the model predicted loss pattern after recovering with FEC. We use trace-based simulation. This evaluation process is usually used to estimate the overall loss model performance in respect to multimedia service quality (see [11], [5]). The trace-based simulation consists in collecting loss patterns resulting from real traffic transmission over the network. In our case, we use a WLAN network to collect the loss pattern resulting from real H.264 video multicasting. Each transmitted RTP packet contains a sequence number that is intended for intra-media synchronization. If the packet arrives, the receiver will write the sequence number into the trace file. Afterwards, during an off-line analysis, we calculate the final loss pattern file and then divide it into several segments (corresponding to the extended RTCP reports). The simulation of streaming server behavior is obtained after a multi-pass processing. The number of processing passes is tightly dependent on the number of loss pattern segments. We use a more precise model’s evaluation process by measuring the accuracy gap between the experienced and the model-predicted results. In other words, each time we measure a new loss pattern segment (receiving RTCP feedback), we calculate the distance between the variations of both measured and model-predicted loss patterns; then, by making a summation of these distances, we obtain a surface that roughly represents the accuracy gap.

1.

Sliding Number of Model’s States (SNMS)

To evaluate the performance of a network with respect to realtime video application, a model with a limited number of states is sufficient (for example, authors in [11] show that their Internet packet traces typically match with a 6 states model), which optimize memory and computational capabilities of the system that performs modeling. Meanwhile, it was previously shown that models with more states capture better the long burst loss effect (see [5]). In order to face this issue, we propose an adaptive number (n) of model’s states to accurately capture the fluctuating channel conditions while minimizing the computation and memory cost. We conduct experimentations where the number of models’ states is fixed to a maximum of 50 states for both models. The model with static number of states uses 50 states to capture the channel fluctuation, while SNMS varies the number (n) of model’s states according to the RTCP feedback (i.e. n values is fixed according to the maximum loss run length experienced by the receiver during the last transmitted N packets — see formula (4)). (4) n = {Max(i ) i ∈ [2, 50] and mi > 0} Here, mi denotes the number of loss bursts having length i. Tab. 1: Percentage of lost packets unrecoverable by FEC. Experimented Adaptive States Model 5.07%

4.04%

Static States Model 4.12%

We observe that SNMS behaves globally like the model with static number of states (the overall performance is depicted in Tab. 1). SNMS still provides loss pattern predictions very close to the experienced loss pattern for a reduced memory and computation cost. As an example, in our experimentation, the model with static number of states used an average of 10 kb memory while SNMS used only 2.5 Kb. Note that the higher the memory used in modeling the higher the computation cost is. In the remaining of the paper we systematically use the SNMS in both loss and ILD models.

2.

Adaptive Loss Pattern Prediction

This section presents a design of fuzzy system that predicts the next loss pattern segment. Consider a variable vector M (corresponding to burst loss length occurrences vector) that assumes the sequence of the following values:

{M } = i −1

M 1, M 2 ... M i −1

(i-1) is the number of received feedback reports.

We use the exponential weight mean average (EWMA) of the precedent sequence {M i −1 } to predict the next value of M (Mi). (5) M i = (1 − α )M ( i − 1 ) + α. Μ ( i − 1 ) The predictor, illustrated in equation (5), is controlled by a vector parameter α, where α is the weight given to past history (α is also considered as the smoothing factor). The larger it is, the more weight past history has in relation to the last observation. It is much suitable to keep certain influence of past history in order to smoothly react to ephemeral channel fluctuations. Although statistical characteristics of real channels can significantly vary with time, propagation experiments for various types of channels [8], indicates that the basic system parameters remain constant over short time intervals. A major problem with the exponential averaging predictor is in the choice of α. It would be useful to automatically determine a ‘good’ value of the smoothing factor α, and to be able to change this value online if the loss distribution fluctuates. Our approach uses fuzzy logic control to achieve this tuning. The fuzzy controller is effective and costless in term of computation latencies, which is much suitable for stringent network services such as multimedia streaming applications. Our proposed fuzzy system enables run-time adaptation based on iterative feedback control and knowledge acquired from past experience. In our case, the fuzzy controller behaves like a minimization function. Thus, we vary the vector α in order to minimize/cancel the error vector between the last measured loss pattern segment Mi and the last predicted loss pattern segment Mi . min M i - M i / {α}j ∈ [0,1]

(6)

Hence, we simply replace Mi by Mi in (5), and then figure out the appropriate {α}j vector elements. In other words, we correct the error produced in the last step through finding out the appropriate α vector that should have been employed – see equations (6) and (7). (7) α.( M i −1 − M i-1 ) = M i- M i-1 / {α } j ∈ [0 ,1] The resolution of the previous equations will permit to find out an adequate vector α; each element {α}j in the vector α varies independently according to the observed occurrence of burst length j during past measurements. By casting the problem into a fuzzy-based control theory framework, we obtain a simple SISO (Single Input Single Output) system. The input represents the elements of the error vector ( M i - M ) and the output represents the {α}i vector elements. i

This means that when the error (i.e. difference between the predicted and the measured loss segments) is high we should increase α in order to give further weight to recently measured loss segments. Otherwise, if the predicted loss segment is close to the measured loss segment, we decrease α to keep a high influence of the past history. This way, α elements fall around zero when the channel is in

Simple model

Fuzzy-based adaptive model

0.1607

0.1414

We evaluate the performance of the combined-model loss prediction through extensive trace-driven simulation. In order to better quantify the achieved performance, we give in Tab. 2 the accuracy gap between the fuzzy-based adaptive model and the simple model (i.e., that does not consider past history). This is a good indictor of the overall “short-term” model predictions accuracy. The presented values are obtained after averaging the results of 10 trace-based simulations; each time, we use a different loss pattern that is collected from a real WLAN communication. For each simulation, the accuracy gap between the experienced and predicted loss pattern is evaluated using the runge-kutta numeric integration method; it basically makes a summation of the distances between two functions. The accuracy gain of the fuzzy-based adaptive loss distribution prediction is around 12%.

25

50 45

20

40 mean inter loss distance (MILD)

Tab. 2: Accuracy gap between measured and estimated loss patterns (x103).

Fig. 4 during the periods delimited by the receiver reports: “8” to “16”, and “396” to “404”). This is particularly noticeable when both the measured ILD and loss-run-length distribution are stable over the time, which increase the FEC efficiency (see Fig. 5 and Fig. 6 between the receiver reports “8” to “16”). The mean measured loss rate during the multicast streaming session is around 6.1% (i.e., 7607 lost packets). When using the conventional adaptive streaming server, the mean perceived loss rate reached over 5% (6210 lost packets after FEC recovery). At the same time with our proposal, we notice a perceived loss rate of about 4% (4968 lost packets after FEC recovery). This difference in mean loss rate (1%) can have devastating consequence on video streams. As the wireless channel may exhibits too long periods of perturbation (unavailability), our model don’t account for the excessive losses that are brief spikes. Thus MBL values are limited by 15, so larger values are filtered by the fuzzycontroller, and thus not considered in predicting the next loss pattern segment. MILD values are limited by the maximum number states of the ILD model, i.e., 50.

mean burst length (MBL)

stationary period, which comes out through stable and quite correlated successive loss segments. The shape and the range of the fuzzy membership functions should be tuned according to the max burst error. We recommend using a Gaussian membership function that provides a broad choice in setting the coarseness/smoothness of α adaptation. One may tune the function until reaching an acceptable behavior in respect to the wireless environment.

15

10

5

35 30 25 20 15 10 5

0

0 50 100 150 200 250 300 350 400 450 Loss pattern segment index (corresponding to receiver report for N = 300 packets)

V. PERFORMANCE EVALUATION In this section, we evaluate the implementation of our streaming system through trace-based simulations. We emphasize the ability of our adaptive combined model to foreseen wireless LAN fluctuations. The simulation is performed for a streaming scenario where a multicast server distributes video together with FEC. To this end, we use video loss traces collected in real WLAN communication.

1.

Experimental Results Analysis

Fig. 4 depicts the instantaneous application-level loss rate (in percentage) measured when using our proposal versus the conventional method. We include the loss rate curve when no FEC is used in order to better discern the possible loss recovery gain. The NoFEC curve represents, indeed, the instantaneous loss pattern segments perceived by both adaptive streaming systems. This loss rate (green curve with square marker) will actually steer the streaming adaptation as it basically represents the successive reported loss pattern segments. The measurements are depicted for 12 min of CBR Soccer sequence streaming, while the reported loss rate values are each time averaged over 300 packets. Note that the loss rate curves represent final packet loss rates (FLP) measured after recovering with FEC.

Fig. 5. Instantaneous MBL.

0 0 50 100 150 200 250 300 350 400 450 Loss pattern segment index (corresponding to receiver report for N = 300 packets)

Fig. 6. Instantaneous MILD.

It is clear that our streaming system provides better communication robustness through increasing the loss resiliency, which certainly mitigates multimedia quality degradation in harsh wireless conditions. However, in most of existing wireless multimedia services deployment the bandwidth consumption can be a matter of concern as well. Consequently, the tradeoff between bandwidth consumption and perceived quality must be carefully investigated. Fig. 7 illustrates the instantaneous measured throughput during the multicast streaming session with our proposal (and respectively the conventional streaming system). The mean bandwidth consumption achieved with the conventional streaming system is around 495 Kbps, while the bandwidth consumption is about 510.5 Kbps with our proposal. This excess bandwidth usage (3.1%) can be afforded by Service Providers (SP) if the video quality continuity is sustained. Additionally, in almost all service-level-agreement networks, the WLAN resources are usually dedicated to sensitive multimedia services. 900 850

Conventional adaptive FEC Our proposal

90

application-level perceived loss rate (%)

Bandwidth consumption (xKbps)

Witout FEC transmission Conventional adaptive FEC Our proposal

80 70 60 50 40

800 750 700 650 600 550

30 500

20 450

0 50 100 150 200 250 300 350 400 450 Loss pattern segment index (corresponding to receiver report for N = 300 packets)

10 0

0

50 100 150 200 250 300 350 400 Loss pattern segment index (corresponding to receiver report for N = 300 packets)

450

Fig. 4. Instantaneous receiver-perceived loss rate.

We observe a high loss resiliency with our proposal when the channel burstiness passes through stationary period (for instance, see

Fig. 7. Instantaneous bandwidth consumption.

It is commonly accepted, from the Service Provider point of view, that additional bandwidth consumption is tolerated only for enhanced loss resiliency and video quality at the receiver side. For the purpose

of measuring the tradeoff between the useful received data and the transmitted data, we define the FEC efficiency factor as:

FEC − EF =

Total Data Re ceived Data + Transmitted FEC

Here, the total data represents the obtained video packets after recovering with FEC (i.e., received packets and recovered packets). So ideally FEC-EF = 1 when (i) no FEC is transmitted and the communication doesn’t suffer from losses or (ii) all transmitted FEC redundancy is used to recover from packet losses. Fig. 8 illustrates the measured FEC-EF throughout the multicast streaming session. 1 0.9

FEC-Efficiency Factor (