Abstract—This paper proposes an algorithm for the adaptive adjustment of the transmission rate of VoIP sources based on the voice quality estimated at the receiver. This adjustment is achieved through the appropriate use of differing voice codecs, as the conditions of the network change, in order to maintain an efficient utilization of the available resources. To validate our proposal realistically, we have made an effort to simulate VoIP calls using sources that follow Brady´s model of human conversations. We investigate the effects of the proposed model on the aggregate network traffic and compare the results with existing related work. Simulation results show that the proposed algorithm makes better use of the available bandwidth, achieving superior performance in comparison to similar works.
I. INTRODUCTION
OIP (Voice over Internet Protocol) is an increasingly popular service for voice calls over IP networks. This includes signaling for establishing and completing each call, as well as digitalizing, coding and packing the voice signal so that it can be transmitted by the data network. While VoIP services are commercially attractive due to their low cost, their success will be influenced by consumer satisfaction, in relation to the quality of the calls, and how closely this quality compares to that of conventional fixed or cellular telephone services. In addition to economic motivations, the integration of data and telephony networks makes it possible to offer new services (e.g. voice mail, instant messaging, conferencing, etc.). However, the inability of initial VoIP implementations to cope with the unpredictable nature of the Internet, seriously affected the acceptance of early VoIP telephone services.
Given the Internet is based on the best effort service model, there are no guarantees about the delivery nor the delay experienced by packets while traveling through the network. Thus, it is a challenge to have VoIP services offer the same quality of service (QoS) as a conventional telephony network, i.e. reliable and with quality of service guarantees. QoS for VoIP services has been at the center of attention in numerous research efforts [4], [5], [6], [10], [11], [12], [15] and [20].
Three main factors affect the perceived voice quality at the receiver: the total end-to-end delay which depends on the VoIP architecture, and is influenced by a variety of parameters such as packet size, coding algorithm, playout buffer size and network characteristics (e.g. latency, bandwidth, network topology and configuration); the effect of delay variability (or jitter); and the packet loss rate (given
* Supported by post graduate scholarship from CAPES.
that the Internet is based on a best effort model and does not reserve resources, it is prone to suffer from congestion and thus loss of packets).
Transmission of voice over data networks is possible through different voice coding techniques. These codecs generate a constant data rate during the speech periods, independent of the network conditions. Note that the choice of voice codec used by an application will limit the maximum attainable QoS level. Moreover, as network conditions worsen, so will the QoS. Existing applications are generally hardcoded with a specific codec to provide a reasonable QoS under typical average network conditions. Given that these conditions can vary abruptly [19], the QoS experienced by users is significantly worse than expected. One approach to solve this problem is to use applications that try to match the transmission rate with the available network capacity. This adaptation can be made by simply changing the codec in use.
The objective of this work is to propose a new algorithm for transmission rate adaptation at VoIP sources based on the voice quality. The algorithm at each source obtains feedback information from its corresponding receiver and makes decisions that aim to maximize the voice quality perceived at receiver. In this paper, this quality is reflected by a MOS (Mean Opinion Score) value (described in Section IV) estimated from statistics of the network and architecture behavior during a VoIP call.
This paper is organized as follows: Section II presents some related work while Section III describes the architecture adopted to model VoIP conversations in a realistic fashion. Section IV presents the proposed MOS approach for monitoring the quality of VoIP calls. Section V outlines a new algorithm for transmission rate adaptation, adaMOS, which takes into consideration the perceived voice quality. Section VI reports on some of the simulations performed. The results obtained demonstrate the good performance of adaMOS in different scenarios. Section VII presents final considerations, conclusions and future work.
II. RELATED WORK
Adaptive sources, based on dynamic rate control with feedback, for multimedia flows have been studied for some time [2], [17], [23] and [25]. This class of mechanism tries to match the transmission rate with the available network capacity, aiming to minimize congestions. Results show that adaptive applications are more robust and efficient in presence of congestion, and thus able to transmit more audio streams while maintaining an acceptable QoS level.
In contrast, relatively little work has been carried on feedback-based rate adaptation for interactive voice
MOS-Based Rate Adaption for VoIP Sources
N. T. Moura*, B. A. Vianna*, C. V. N. Albuquerque, V. E. F. Rebello and C. Boeres Instituto de Computação – Universidade Federal Fluminense – Brazil
{nmoura,bvianna,celio,vinod,boeres}@ic.uff.br
communication such as VoIP. Barberis et al. [1] proposed an adaptive transmission rate algorithm based on network measurements, with the intention of detecting temporary congestions and estimating the network’s bottleneck link capacity. Based on this information, the adaptive algorithm selects a transmission rate compatible with the network capacity. Simulations show that the algorithm is able to react rapidly to changes in the network state, but can arrive at the wrong conclusion. For example, increases in network delay do not necessarily harm voice quality. It is known that for a total end-to-end delay in the range from 0 to 150 ms, the MOS shows little variation [22].
A new, but relatively simplistic, quality of service control scheme was proposed by Qiao et al. [18] for use in a
differentiated service network (DiffServ) [11] by combining
a adaptive transmission rate strategy (through the use of Adaptive Multi-Rate codec (AMR) [7]) and the marking of voice packets with a higher priority. The algorithm, however, assumes that all sources transmit at the same rate and, when a rate adjustment is performed, it occurs simultaneously at all sources. This constitutes a serious limitation, since it assumes that all communication share the same network path and an implementation of such a system requires a global controller, which is not practical in IP networks. Another contribution of their work was the adaptation of the transmission rate based on an estimated MOS observed at the receiver. However, as discussed in Section IV, it is unclear how closely this estimated MOS (which only considers packet loss [24]) approaches the real MOS. Furthermore, their architecture does not use a playout buffer to reduce the effects of network jitter.
In contrast with existing rate adaptation mechanisms, this paper introduces a new algorithm for transmission rate adaptation for today’s Internet and validates this proposal in a simulated environment. The adaptation is completely distributed and independent for each VoIP communication. In addition to considering packet loss, this paper highlights the importance of considering the network delay, end-to-end delay and jitter, to obtain a more accurate voice quality measure.
III. THE ARCHITECTURE
Fig. 1 illustrates the architecture proposed for the adaptive control of the VoIP source coding rate introduced in this work. At the source, the voice is digitalized, encoded and packed to be transmitted by the network. RTP/UDP/IP protocols are then responsible for delivering the voice packets to their destination. When arriving at the receiver, the jitter is removed by a playout buffer and packets are then decoded. At the receiver’s side, information about delay, loss and estimated MOS is collected and returned to the source, where the proposed adaptive transmission rate control algorithm (adaMOS) will determine the appropriate transmission encoder to maximize the voice quality perceived by the receiver. In the following sub-sections the main elements of this architecture will be described in detail, namely: the VoIP source, the VoIP receiver and the feedback mechanism.
Source
Receiver Rate Controler (adaMOS)
Coder Decoder IP Network Feedback Information IP Network Via RTCP
Fig. 1. The proposed adaMOS architecture
A. The VoIP Source
Another limitation observed in the works cited in Section II is the representation of voice flows as continuous data. Given that human conversation consists of alternating speech and silence periods, it is more appropriate for sources to employ silence suppression techniques and only transmit if a speech period is detected. This on-off behavior can have a significant effect on aggregate voice traffic. In [3], Brady showed that the speech and silence periods can be approximated by an exponential distribution with an average of 1/λ (ON) and 1/µ (OFF). λ-1 = 1.004s and µ-1 = 1.587s, are typically assumed values, which include the hangover time
(i.e. any period of silence with a duration less then 200ms is considered part of a speech period). In this paper, the use of sources that reflect this voice model is considered and the effect of aggregated network traffic from multiple sources is investigated. Each source employs the proposed adaMOS adaptive algorithm to adjust its transmission rate.
B. The VoIP Receiver
Each of receivers used in the proposed architecture employ a playout buffer with the objective of minimizing the effect of jitter on the voice quality. Two adaptive playout buffer techniques were studied and implemented in the simulation environment in order to model receivers more realistically in the proposed architecture and to evaluate the behavior of playout buffers in face of VoIP sources that adjust their transmission rates adaptively. The first technique is based on the work in [19], where the playout delay (the time between when the packet is generated and played) is adjusted only between talk spurts. This mechanism is referred to hereinafter as a talk spurt buffer. The second variant is a technique where the playout delay is adaptively adjusted, in accordance with the network conditions, for each individual packet even during a talk spurt as proposed in [13]. This mechanism is referred to as a packet playout buffer in the remainder of this text.
C. The Feedback Mechanism
The receivers periodically send feedback information (in the form of a feedback packet) to their respective sources, through the RTCP protocol. This information does not include explicit congestion information related to the state of intermediate nodes. At the end of each feedback period, each receiver calculates the arithmetic mean delay experienced by the packets, the packet loss rate and an estimate of the MOS
experienced within that period. The feedback packet also includes information on the coding rate of the packets received during the period. If the feedback period is very short, there will be a large data flow from the receivers to the sources. On the other hand, a very long interval will cause the algorithm to be less receptive to rapid changes in network conditions and thus compromise the perceived quality of service significantly. In the future, piggybacking feedback information in voice packets for bi-directional VoIP conversations will be investigated.
IV. QUALITY MONITOR
Recent research, such as those cited in Section II, has focused on VoIP applications that adapt their configuration to the state of the network with the goal of maximizing the quality of calls. However, a key issue has been how to measure voice quality, and therefore design adaptive applications based on this metric. Earlier VoIP applications equated quality with information from measurements from the transport or network layer, e.g. packet loss or network delay. However utilizing either one of these separately does not reflect faithfully the quality of service perceived at the receiver.
Clearly,a good quality of service measure for VoIP calls must somehow consider the user’s opinion about the service. The Mean Opinion Score (MOS) is a method recommended by the ITU-T P.800 to measure speech quality. In this method, the users rate the call quality in a range varying from 1 (bad) to 5 (excellent). However, this is a very subjective method that consumes a significant amount of time and consequently is both very costly and inappropriate for on-line adaptations.
Other models aim to determine this score in an objective way.Amongst them, two are well established: the E-Model [9] and the Perceptual Assessment of Speech Quality (PESQ) [21]. It would be interesting to at least be able to estimate the quality perceived by the users of the VoIP application, based on quantitative data available, such as the packet loss rate and/or the delay experienced by packets. This work employs Equation (1) (shown previously to be a good approximation for the MOS value [8]) to map network parameters to an estimated MOS value:
3 2
d
d
d
p
T
MOS
≈
−
α
+
β
−
η
+
ϕ
(1) where α=0.195, β=2.64x10-3, η=1.86x10-5, ϕ=1.22x10-8, p is the packet loss rate, d represents the total end-to-end delay and T represents the maximum MOS value for a given codec (i.e. when the transmission experiences no losses or delays).The objective of the adaptive transmission rate algorithm, adaMOS, is to maintain the estimated MOS value at the highest level for the duration of the communication. The voice quality must be at least acceptable (above 3.6) and, for tolling purposes, it is desirable that the MOS remains above 4.0 [14].
V. ADAMOS: THE ADAPTIVE ALGORITHM
The adaptive transmission rate control algorithm, adaMOS, takes into consideration the network and
end-to-end delay and the packet loss rate in the form of an estimate of the voice quality at the receiver, in order to make informed decisions regarding changes in the coding rate. These values are returned to the source through the feedback mechanism. The basic idea consists of reducing the source transmission rate when high values of delay, packet loss or a reduction in the estimated MOS are perceived. Moreover, the algorithm may increase the transmission rate and thus voice quality when perceiving that the current network conditions will support it. Note that this work also employs a playout buffer to reduce the effects of network jitter and improve voice quality. To overcome the effects caused by the buffer delay, the algorithm calculates two different MOS values using Equation (1): net_MOS, an estimated MOS value prior to the playout buffer based on the packet loss and network delay, and; estimated_MOS, an estimated MOS value that considers the buffer delay and thus reflects the voice quality experienced by the listener. While net_MOSis used to detect congestion, or the possibility of it, and thus initiate a rapid decrease in the codification rate, estimated_MOS is used to trigger conservative rate increases. It is the combined use of these metrics that provides adaMOS with a solid foundation for making decisions. Table I gives a description of the variables of the adaMOS algorithm presented in Fig. 2.
TABLE I
ADAMOS PARAMETERS AND VARIABLES
Parameter Description ALFA_ADAPTIVE weighting factor for the smoothed average of the
network delay and packet loss
MAX_LOSS maximum acceptable loss – triggers a rate reduction MIN_LOSS value at which the algorithm might wish to consider a
rate increase
DELAY_THRESH relative delay value that triggers a rate decrease (increase) when delay increases (decreases) INCREMENT_THRESH score required to allow a rate increase – this value
determines how conservative the algorithm is to transmission rate increases
net_delay average network delay during last feedback interval loss_avg smoothed loss ratio delay_avg average (smoothed) delay decrement flag to indicate a moderate rate decrease halve flag to indicate a significant (by half) rate reduction increment indication of the degree to which the network can
support a rate increase
limit_rate the rate that lead to the last rate decrease backoff_time waiting time before a rate increase backoff_limit limits the range of values for the backoff_time
adaMOS uses the additive increment/multiplicative
decrement paradigm, similar to some congestion control
algorithms such as TCP. It is important to notice that the multiplicative decrease factor is fundamental for concept of fairness, since systems should aim to distribute bandwidth appropriately among applications. The additive increment is a conservative approach for increasing the transmission rate without causing a dramatic impact on the network.
This work also considers the use of an exponential backoff technique, with intention to prevent the symmetrical oscillatory behavior of the sources. The idea is that a source that suffers successive rate decreases from the same transmission rate should be prevented from returning to this rate for a certain period of time (the backoff time).
Care must be taken when using the feedback information received since it may not yet reflect the effect of a rate
change. Feedback packets are identified as valid(line 2 of the algorithm in Fig. 2) if they refer to the current codification rate and a sufficient number of the packets (greater than 30% of the maximum number expected) is received during the current feedback interval. One exception is when the codification rate during the feedback period is lower than the current rate and the packet loss rate exceeds the maximum acceptable loss threshold. This situation occurs when a recent rate increase probably overloaded the network capacity, justifying an immediate rate decrease.
1. //Validating feedback packet 2. if ( valid_feedback ) {
3. loss_avg=ALFA_ADAPTIVE*loss_avg+(1–ALFA_ADAPTIVE)*loss_rate; 4. // Check loss rate
5. if ( loss_avg > MAX_LOSS ) { 6. halve = TRUE;
7. increment = 0; 8. }
9. else if ( loss_avg < MIN_LOSS )
10. if ( estimated_MOS > previous_estimated_MOS ) 11. increment++;
12. // Check delay
13. if ( net_delay > delay_avg * DELAY_THRESH ) { 14. if ( net_MOS < prev_net_MOS ) {
15. decrement = TRUE; 16. increment--; 17. }
18. }
19. else if ( net_delay < min_delay * DELAY_THRESH ) { 20. if ( estimated_MOS > previous_estimated_MOS ) 21. increment++; 22. } 23. delay_avg=ALFA_ADAPTIVE*delay_avg+(1-ALFA_ADAPTIVE)*net_delay; 24. previous_estimated_MOS = current_MOS; 25. prev_net_MOS = net_MOS;
26. // Check decrease rate indication 27. if( halve || decrement ) { 28. increment = 0;
29. if( limit_rate == current_rate ) { 30. backoff_limit *= 2;
31. backoff_time = (rand() % backoff_limit) + 1; 32. } 33. else { 34. limit_rate = current_rate; 35. backoff_limit = 1; 36. backoff_time = 0; 37. } 38. if( halve ) { 39. HalveRate(); 40. }
41. else if( decrement ) { 42. DecrementRate(); 43. }
44. loss_avg = 0.0; 45. }
46. }
Fig. 2. adaMOS: adaptive algorithm
1. if(increment == INCREMENT_THRESH){ 2. if((backoff_time>0)&&(current_rate==limit_rate-8000.0)){ 3. backoff_time=(--backoff_time >= 0) ? backoff_time : 0; 4. } 5. else{ 6. IncreaseRate(); 7. increment = 0; 8. loss_avg = 0.0; 9. } 10. }
Fig. 3. Rate increase procedure
For each valid feedback packet received, the adaptive algorithm evaluates the network conditions and the estimated voice quality perceived by the user. If the average loss rate is greater than the maximum acceptable loss threshold (line 5) the current rate is halved (line 6). As well as fairness, the fact that packet loss significantly impact voice quality justifies an aggressive reduction. If the average loss rate is below a given loss threshold (line 9), and the estimated voice quality at the receiver has improved (line 10), an indicator representing the network’s ability to support a rate increase is upgraded (line 11). If the delay is more than a certain threshold above the average (line 13) and the voice quality also suffered a reduction, a rate decrease is signaled (line 14 to 16). If the delay is within a given tolerance factor of the
minimum delay observed throughout the voice call (line 19) and the estimated MOS experienced an increase (line 20), this will be interpreted as an improvement in the network conditions, and thus increase the chance of raising the transmission rate (line 21).
Each time a rate reduction is implemented, the algorithm verifies the transmission rate prior to the last rate decrease. If the potential exists for oscillatory behavior (line 29), a backoff time is chosen to prevent a possible rate increase from occurring in the near future. This backoff time is determined randomly from range which grows exponentially with the number of successive identical rate reductions (lines 30-31).
The rate increase procedure (described in Fig. 3) is only called during periods of silence due to the fact that, based on simulation results, transmission rate modifications during speech periods harm playout buffer performance, in particular the talk spurt buffer. Since estimated playout delay (based on the first packet of the spurt) of a talk spurt buffer is only adjusted between speech periods, any change in the coding rate during a talk spurt may cause the playout buffer to discard packets, until the next talk spurt. A rate increase only takes effect if a sufficient number of positive indications has occurred (i.e. reached the increment threshold) (line 1) and the previous backoff time has expired (line 2).
VI. SIMULATION RESULTS
This work opts to use the same simple network topology (Fig. 4) used in [1] for the simulated evaluations, since complex topologies, though potentially more realistic, have the disadvantage of generating results that lead to inconclusive interpretations. Moreover, the use of this topology provides a basis for comparison with the previously cited work. In this topology, the link between switches SW1 and SW2 is the bottleneck link. This link has a configurable bandwidth (L), and a configurable latency (D). Access links are assumed to have enough bandwidth for all the traffic traveling from a source (Fi) to a destination (Ri) and present fixed delay of 1ms.
Fig. 4. Simulated topology
In the following sub-sections, a number of simulation scenarios is presented highlighting the performance of the algorithm proposed in this work. The NS-2 simulator [16] has been extended to attend the necessities of our architecture. All scenarios were simulated for 900 seconds, with a feedback interval of 1 second, using Brady´s voice model and with both adaptive playout buffer techniques. The results for both playout buffers showed a similar behavior relative to the estimated MOS, however due to space
constraints, only the results using a packet playout buffer are presented.
A. Scenario 1: Aggregated Sources
In this first scenario, the behavior of 20 simultaneous VoIP transmissions is evaluated. The results are compared with the algorithm proposed in [1], denoted by AVoIP, for an identical architecture which included packet playout buffers. Fig. 5 represent the average network delay over the 20 sources using simulation parameters of L=256kbps and D=3ms. Notice that AVoIP introduces an oscillating behavior in the network. A possible explanation for this instability is the use of on-off sources that model the conversation in a realistic way in our architecture. AVoIP was designed to handle constant bit rate voice flows. However, the realistic on-off behavior of several aggregate sources has a negative effect on AVoIP since inappropriate decisions to increase the transmission rate are taken at specific moments when a number of sources are in the off state. The adaMOS approach for rate increases is conservative and avoids making rash decisions. Consequently, as observed in Fig. 6, the perceived voice quality is maintained stable at a superior and acceptable level. In this scenario, adaMOS experienced no packet loss, while AVoIP incurred an average packet loss of 0.94% and at certain moments spikes, peaking as high as 58%, were observed. 0 20 40 60 80 100 120 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950 1000 Network Delay (ms) Time (s) adaMOS AVoIP
Fig. 5. Average network delay with 20 sources
B. Scenario 2: Bandwidth Influence
This scenario increases the number of simultaneous VoIP transmissions to 100 with the objective of analyzing the effects of available bandwidth on the average packet loss and estimated MOS. The simulation used a main link delay of 100ms. Fig. 7 show the playout MOS for 8 codecs without adaptation (fixed rate), adaMOS and AVoIP.
Most interestingly, notice that when bandwidth is scarce (below 3000kbps) adaMOS causes lower network delays and loss rates than AVoIP and thus achieves a better utilization of the available bandwidth. Also notice in Fig. 7 that the adaMOS quality (estimated MOS) is always above the minimum level for acceptability (MOS=3.6 is also a value from where tolling becomes possible). As expected, while low fixed rate codecs exhibit worse performance when there
is plenty of available bandwidth, high rate codecs are worse when the available bandwidth is insufficient.
0 1 2 3 4 5 0 200 400 600 800 1000 MOS Time (s) adaMOS AVoIP
Fig. 6. Average MOS quality with 20 sources
3 3.5 4 4.5 5 0 1000 2000 3000 4000 5000 6000 MOS Bandwidth (Kbps) adaMOS 8kb 16kb 24kb 32kb 40kb 48kb 56kb 64kb AVoIP
Fig. 7. Average MOS quality for 100 sources
C. Scenario 3: adaMOS Operation
This scenario illustrates the voice quality (MOS) of adaMOS as a function of both the network latency and the available bandwidth. As expected for 100 simultaneous VoIP conversations, Fig. 8 shows that high bandwidth low latency networks provide higher quality connections than low bandwidth high latency networks. The shaded 2D region below the plotted surface identifies the range of latency/bandwidth values for which the voice quality is acceptable (i.e. MOS of 3.6 or above). For network latencies under 120ms, adaMOS attains MOS values superior to 3.6, even with a restricted bandwidth of 400kbps for 100 sources.
Fig. 9 compares adaMOS and AVoIP performance for differing bandwidths and network latencies. The area above each line represents again the range of bandwidth and latency combination for which the estimated MOS is acceptable. Note that adaMOS achieves the acceptable MOS value with lower bandwidths and higher latencies when compared to AVoIP. As the available bandwidth increases, both algorithms deliver acceptable qualities (higher than 3.6).
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 50 100 150 200 250 300 350 400 450 500 0 1000 2000 3000 4000 5000 6000 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 MOS Latency (ms) Bandwidth (Kbps) MOS
Fig. 8. MOS x Latency x Bandwidth
0 500 1000 1500 2000 2500 3000 0 50 100 150 200 250 300 Bandwidth (Kbps) Latency (ms) adaMOS AVoIP
Fig. 9. Delay x Bandwidth – adaMOS and AVoIP for a MOS of 3.6 VII. CONCLUSIONS AND FUTURE WORK
This work proposed a new adaptive algorithm for the transmission rate adjustment of sources for VoIP applications on the Internet. The principal contribution of the proposed algorithm is the fact that it not only takes into consideration network parameters, but also the voice quality perceived by users unlike existing mechanisms such as AVoIP [1].
Different from the architecture proposed in [18] (which is limited to DiffServ networks), this proposed architecture supports the use of playout buffers. The feedback-based adaMOS algorithm has been shown to be robust in a variety of test scenarios. These simulations clearly showed that the received voice quality is a fundamental criterion for decision making. Our conservative approach when increasing the transmission rate is important in many scenarios, especially if realistic on-off aggregated sources are considered.
The adaMOS algorithm for adaptive rate control of VoIP sources revealed promising results and future work to investigate characteristics such as sensitivity, stability and fairness in scenarios with realistic topologies and interfering traffic is underway. This will be an important step to certify the algorithm’s aptitude for the current Internet.
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