Improving TCP-Friendly Rate Control in Wired and Wireless Networks By a Scheme Based on Wireless Signal Strength

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1 Improving TCP-Friendly Rate Control in Wired and Wireless Networks By a Scheme Based on Wireless Signal Strength Il Mo Jung, Nicolaos B. Karayiannis, and Steven Pei Department of Electrical and Computer Engineering, University of Houston, Houston, TX, , USA {ijung, karayiannis, spei}@uh.edu Abstract We propose a rate control scheme developed to improve TFRC (TCP-Friendly Rate Control) in wired and wireless networks. Next-generation wireless users will expect multimedia streams of high quality as well as high mobility that has time-varying wireless error rate. The proposed scheme is designed to meet such requirements. This is accomplished by using wireless signal strength information to improve wireless TFRC performance. Most wireless devices currently have wireless signal strength information in the driver level. In Windows XP, our scheme can be implemented by a slight modification of the TFRC algorithm in the application level through the use of WRAPI 2.. To evaluate the performance of the proposed scheme, we simulated several cases over a wireless network via the NS-2 simulator and compared the efficiency of delivery exhibited by the proposed scheme with that of existing schemes. The proposed scheme is robust to congestion loss and wireless loss. In addition, our scheme exhibits stable performance even in timevarying wireless conditions. 1. Introduction TFRC is the scheme that allows us enjoy multimedia stream over the Internet. However, TFRC is based on the loss and delay that can be measured exactly only in wired networks. As a result, its performance is not reliable in wireless networks. Our work was especially focused on users with high mobility. Currently, some network service providers began to provide a wireless metropolitan area network service. As a result, we cannot assume that the wireless This work was supported by the National Institute of Justice under Grant 23-IJ-CX-K11. error rate is semi-static due to the users high mobility. TFRC should be enhanced to be able to handle such high mobility. There have been various approaches to dealing with this problem in the network layer [1], [2], [3]. F. Yang et al. [4] used link layer as well as network layer information to discriminate packet delay and loss due to erroneous wireless channel. Although this approach is more accurate than previous approaches, it depends on network hardware and its open application program interfaces (APIs). Our approach to this problem uses wireless signal strength information that can be obtained without any hardware dependency in Windows XP. For example, Windows XP offers the Wireless Zero Configuration service, which handles wireless connections. This service can be substituted by a simple middleware using WRAPI 2.. This middleware can be designed to have a dual role, that is, to deal with the wireless connection and to provide real-time signal strength as a type of system function. Such middleware is simple and makes it possible to control the streaming rate in the application layer in a way similar to that the streaming rate is controlled by several existing TCPfriendly rate control schemes. Round-trip time (RTT) over the wireless Internet can change dramatically depending on the wireless environment. Most schemes developed to deal with this RTT characteristic are very conservative when it comes to updating its value. This can prevent undesirable increases of RTT. Ironically, this can be a disadvantage, since more time would be required to reduce the RTT even though a small RTT value is being measured. This paper introduces a rate control scheme that relies on wireless signal strength information to filter packet delay due to automatic repeat request (ARQ) and losses due to wireless channel error. The rest of this paper is organized as follows: Section 2 presents

2 the proposed signal strength-based rate control scheme. Section 3 presents simulation results that validate the proposed scheme by comparing its performance with that of existing schemes. Finally, Section 4 contains concluding remarks. 2. Signal Strength-Based Rate Control We propose a signal strength-based rate control scheme, which may be used to filter packet delay due to ARQ and losses due to wireless channel error. Although signal strength-based rate control may be seen as one of the cross-layer approaches, it can perform in the application layer only. Windows XP can provide system API using WRAPI 2.. There is certainly a way to access signal strength information in Linux. Currently almost all wireless devices use signal strength information. In the case of Windows XP and Linux, at least, we can use signal strength information without any hardware/system specific APIs. The proposed signal strength-based scheme is based on the wireless signal strength level. If the wireless signal is weak, it would be hard to formulate packet delay and loss between wireless nodes through the use of network layer parameters only. This became evident from our simulation results. For example, if a fastmoving mobile node moves away from its access point within coverage and comes back, then the delay and loss of packets transferred over wireless Internet may increase and decrease dramatically. We also observed that if there is a road between the access point and a wireless local area network (WLAN) client, the signal strength may weaken even if the client is not moving fast. Such a situation may be encountered whenever a car passes through that way or the client approaches toward a parked car or some structures. This implies that outdoor wireless conditions are time-varying, at least in the case of WLANs. Existing network layer approaches use network layer measurement values such as loss, jitter, RTT, and statistical values obtained by combining them. Their loss differentiation algorithms (LDAs) differentiate losses by restricting the congestion model over a wired network. So, if a loss is not in a type of restricted congestion models, existing approaches attribute the loss to bad wireless conditions. However, the actual congestion loss and wireless loss are not always exclusive when they are represented by network layer values only. In other words, their LDAs do not work well in high-loss conditions. Meanwhile, a signal strength-based scheme is an entirely different approach in that it relies on filtering instead of attempting to differentiate losses. More specifically, our approach filters losses based on the signal strength. This works Figure 1. Quadratic and linear models of signal quality and strength well for high error rates as well as low error rates. In WRAPI2., signal strength information is derived from wireless received signal strength indicator (RSSI), which is known as the most accurate indicator for predicting the performance of streaming video over a WLAN [5]. The proposed signal strength-based scheme uses signal strength information in dbm (dbmiliwatt). RSSI values can also be changed to dbm, which has a relation with signal quality (-%) [6]. This relation can be described by either a quadratic or a linear model (see Figure 1). Although it is difficult to formulate packet loss rate in terms of signal quality, it can certainly be argued that good signal quality or high signal strength helps the successful delivery of packets whereas poor signal quality or low signal strength makes packet delivery difficult or even impossible. Thus, we assume that signal quality is proportional to the probability that a packet is delivered without error. The signal strength-based scheme relies on a new signal quality model, which is used to filter wireless loss and delay. Although it is similar to the quadratic model, the signal quality model used in this work is much more optimistic. 2.1 Signal Strength Level The dbm (db-miliwatt) is a logarithmic measurement of signal strength. dbm values can be directly converted to and from mw values using the formula below: dbm = 1 log(mw). (1) Wireless signal strength information can be obtained from the WRAPI 2. [7] library that can be installed in Windows XP. A middleware can retrieve signal strength in dbm periodically from the WRAPI 2. library. The TFRC client, equipped with the middleware, can obtain signal strength information in real time. However, there are significant fluctuations of signal strength values in dbm. The signal strength levels can be prevented from fluctuating via Algorithm 1, which is described below:

3 Algorithm 1: Signal Strength Level Let ss = signal strength value in dbm Let sl = signal strength level if ss > -2, then sl := 1 // Best else if -1 * (j-1) > ss > -1 * j, then sl := j for j=3..9 else sl := 1 // Worst end Algorithm 1 assigns to the signal strength level nine values ranging from 1 (the best) to 1 (the worst) except of the value 2. Through the use of signal strength level, we can reduce considerably the frequent fluctuations of measured signal strength values and simplify rate control. For a signal strength level between 1 and 6, most schemes work well. However, the packet loss event rate due to wireless channel error is actually very low. 2.2 Loss Filter Based on Signal Strength Level In our scheme, we deal with the signal quality as a packet delivery probability. Compared with the quadratic model shown in Figure 1, our scheme is much more optimistic in defining signal quality in terms of signal strength. Low layer error recovery algorithms such as ARQ allow us to assume wireless packet delivery optimistically. More specifically, signal quality is defined according to Algorithm 2: Algorithm 2: Signal Quality Let sl = signal strength level Let sq = signal quality if sl <= 5, then sq := 1. // Best else if sl = 6, then sq :=.97 else if sl = 7, then sq :=.93 else if sl = 8, then sq :=.5 else if sl = 9, then sq :=.3 else sq :=.1 // Worst end Given the signal quality, we propose the wireless loss filter algorithm shown below: Algorithm 3. Wireless Loss Filter Let rc = received packet count since previous loss filtered was detected. rc :=.1 // almost zero if new loss is detected, if ((rc =.1) or (1./rc < 1. sq)) then rc:=.2 // almost zero, but not first return Filtered else end return Not Filtered end if end if In general, a congestion loss event may involve more than one packet loss when congestion occurs. Algorithm 3 does not filter successive packet losses except of the first loss. Meanwhile, the original TFRC processes one or more packet losses within one RTT as one loss event. As a result, Algorithm 3 can filter wireless packet losses that are distributed uniformly at one signal strength level. If successive packet losses due to congestion occur and enough time has elapsed since the last loss, then the proposed signal strengthbased scheme considers that one of them is wireless loss and the others are just one loss event. In short, the signal strength-based scheme does not filter congestion packet losses occurring successively. However, the signal strength-based scheme does not respond to congestion until successive losses occur. The lack of immediate reaction causes a response delay, which may lower the bandwidth fairness of the signal strength-based scheme instantaneously. 2.3 Delay Filter Based on Signal Strength Level Observing the traffic amount measured in the link layer with a signal strength level between 8 and 1 indicated that traffic may increase up to more than 1 times. This is caused by ARQ, which makes the delaybased loss differentiation algorithms useless. Even in cases where the signal strength level is between 6 and 9, its fluctuation may be severe. Therefore, the original TFRC used a decay factor to update RTT conservatively. The variable df was used as the decay factor for accurate RTT estimate in the tfrc.h file of NS-2 [8]; its value was.95 by default. Here, df is modified using a weight r, which is determined in terms of the signal strength level ( sl ). If RTT increases, then r =.7 for sl = 9 and r =.9 for sl = 1. If not, r =.4 for sl = 9 and r =.7 for sl = 1. Then, RTT is updated as follows: df : = df + (1 df ) r (2) rtt : = df rtt + (1 df ) mrtt (3) where rtt is the round-trip time and mrtt is the latest measured round-trip time. Large values of r make the signal strength-based scheme robust to delay fluctuations due to abrupt weakness of the signal. However, such values of r slow down the response of

4 the signal strength-based scheme to coincident congestion Figure 2. Network configuration 3. Simulation 3.1 Simulation Configuration We used the NS-2 simulator [8] to evaluate the performance of signal strength-based scheme and compare it with that of other schemes in a wireless network environment. The basic network configuration was a dumbbell similar to that shown in Figure 2. In Figure 2, the sender 1 is a TFRC sender. The receiver 1 is also a TFRC client. For mobility, we used the wired-cum-wireless scenario of NS-2. We chose the widely-used shadowing model as our radio propagation model, since we need to create conditions characterized by high interference similar to the moving client s real surroundings. This model is based on the assumption that the received power at a certain distance is a random variable due to multipath propagation effects, also known as fading effects. The environmental parameters of the shadowing model were chosen as follows: the path loss exponent was β = 4, the shadowing deviation was σ db = 7., the reference distance was set to 1., and the seed was set to. Such values are recommended for the obstructed in-building, office with hard partition, or outdoor shadowed urban area. By default, the distance between the base station and each receiver is 1 m. In our simulation, the signal strength was obtained from the class WirelessPhy of the NS-2. The signal strength-based scheme sender was developed by modifying the TFRC Agent of NS Data Delivery Under Semi-Static Wireless Conditions In order to emphasize the mobility properties of wireless network, we conducted a distance-based simulation. In other words, our simulation used the fact that long distance between the base station and TFRC client brings about high error rate of the wireless channel. In this case, only the sender 1 is used. Figure 3 shows the data delivery capabilities of ZBS Received data [1 6 byte] Signal strength TFRC ZBS ZigZag Original TFRC Distance [m] Figure 3. Data delivery under semi-static wireless error rate for 25 seconds Table 1. Error margin of signal strength TFRC in Figure 3, when confidence was 95%. Error margin Error margin kbyte kbps kbyte kbps 1m m m m m m m m m m m m m m m m m m m m m m m m [9], ZigZag [9], the original TFRC, and the proposed scheme for various values of the distance between the base station and the TFRC client for 25 seconds. Table 1 shows the error margin of the proposed scheme, which was obtained when the confidence was

5 95% through the use of the central limit theorem. Here, we used 5 samples, which were measured every.5 seconds. According to Table 1, the error margin is 16.3 ~ kbytes, which corresponds to.512 ~ kbps. As a result, the confidence intervals of the proposed scheme are very small to be seen in Figure 3. According to Figure 3 and Table 1, the proposed scheme has higher data delivery capability than the others except of distance values between 14 m and 16 m. These are the points where low-layer error recovery algorithms begin to fail to transfer data without error. However, even in these cases the proposed scheme exhibited better performance than the original TFRC whereas its differences from the best performing scheme were kbps, 9.59 kbps, and.22 kbps for 14 m, 15 m, and 16 m, respectively. In general, the proposed scheme exhibited slightly higher delivery capacity; this is because it filters every first loss if the signal strength level is high. The proposed scheme exhibited higher performance for high as well as for low signal strength; this is attributable to the high efficiency of wireless loss and delay filtering. 3.3 Data Delivery Under Dynamic Wireless Conditions Real wireless conditions vary considerably with time. Even if a TFRC client does not move at all, its surrounding objects can make the communication with a TFRC client difficult. For simplicity, we assumed that our TFRC client moves away at the 15th second and comes back at the 2th second; this was done in order to simulate dynamic wireless conditions with a speed of 5 meters per second. Figure 4 shows the receiving rate of the ZBS, ZigZag, the original TFRC, and the proposed scheme as a function of time. According to Figure 4, the proposed scheme exhibited the most rapid rate recovery in the signal strength recovery phase. This can be attributed to the filtered RTT. 3.4 Data Delivery Under Congestion Conditions This set of experiments was designed to illustrate that the proposed signal strength-based scheme is efficient and TCP-friendly even in conditions involving congestion. This was accomplished by using two TCP flows, such as those shown in Figure 2. The sender 1, which complies with the signal strengthbased TFRC, communicates from 2 sec to 252 sec. Two TCP flows begin at the 5th sec and end at the 2th sec. Figure 5(a) shows the bandwidth occupied Receiving rate [kbps] by sender 1. The average throughput for the two TCP flows is not obvious from Figure 5 (b) and (c) due to the frequent fluctuations. In fact, the average throughput between 5.5 sec and 2.5 sec was estimated to be kbps for TCP1 and kbps for TCP2. In that interval, the average throughput of our scheme was kbps, which corresponds to 37.26%. This implies that the proposed scheme exhibits good bandwidth fairness. Our scheme occupied a relatively high bandwidth portion (about 3.93%), which is the result of the response delay due to Algorithm 3. According to previous results, the proposed scheme has good data delivery capability in semi-static wireless conditions as well as time-varying wireless conditions. Besides, the proposed scheme remains TCP-friendly. 4. Conclusion Signal strength-based scheme ZBS ZigZag Original TFRC Figure 4. Data receiving rate with moving TFRC client This paper introduced a signal strength-based scheme that may be used to enhance the original TFRC scheme. One of the drawbacks of the original TFRC is that it throttles its sending rate even under wireless losses. In order to overcome this drawback, the proposed scheme relies on wireless signal strength information. Since currently available wireless devices have signal strength information, the information required by the proposed scheme can be obtained in the driver or application level. Even though it is hard to differentiate wireless losses from the signal strength information, we can filter abrupt high loss rate or high

6 Throughput [kbps] Throughput [kbps] Throughput [kbps] (a) Signal strength-based scheme (b) TCP (c) TCP2 Figure 5. Occupied bandwidth trace of the signal strength-based TFRC RTT. To evaluate our scheme, we relied on the NS-2 simulator. Our simulation results indicated that the proposed scheme maintains effectively its TCPfriendly behavior. Besides, our simulation revealed that the proposed scheme exhibited high data delivery capability for almost all cases. This was especially true when the TFRC client had high mobility. In such cases, the proposed scheme recovers its sending rate rapidly since it can presume how high the wireless error rate is and how abruptly RTT changes from the signal strength information. Compared with the proposed scheme, other existing schemes did not respond as quickly to recover their sending rates. 5. References [1] G. Yang, L. Chen, T. Sun, M. Gerla, and M. Y. Sanadidi, Real-time streaming over wireless links: A comparative study, Proceedings of IEEE Symposium on Computers and Communications, Cartagena, Spain, June 27-3, 25, pp [2] M. Chen and A. Zakhor, Rate control for streaming video over wireless, IEEE Wireless Communications Magazine, vol. 12, no. 4, pp , Aug. 25. [3] S. Biaz and N. Vaidya, Discriminating congestion losses from wireless losses using interarrival times at the receiver, Proceedings of IEEE Symposium on Application-Specific Systems and Software Engineering and Technology, Richardson, TX, March 24-27, 1999, pp [4] F. Yang, Q. Zhang, W. Zhu, and Y. Zhang, End-to-end TCP-friendly streaming protocol and bit allocation for scalable video over wireless Internet, IEEE Journal on Selected Areas in Communications, vol. 22, no. 4, pp , May 24. [5] M. Li, F. Li, M. Claypool, and R. Kinicki, Weather forecasting Predicting performance for streaming video over wireless LANs, Proceedings of NOSSDAV 5, Skamania, WA, June 13-14, 25, pp [6] Signal Quality Model: [7] Wireless Research API (WRAPI): [8] NS-2 Network Simulator (Ver. 2). [9] S. Chen, P. C. Cosman, and G. M. Voelker, End-to-end differentiation of congestion and wireless losses, IEEE/ACM Transactions on Networking, vol. 11, no. 5, pp , Oct. 23.