Reliable Video Transmission Over Fading

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... long-term fading (shadowing) and path loss. The multi-path phenomenon generates the amplitude variation of transmitted channel signal, namely the. H.263+.
Reliable Video Transmission over Fading Channels via Channel State Estimation Wuttipong Kumwilaisak, JongWon Kim and C.-C. Jay Kuo Integrated Media Systems Center and Department of Electrical Engineering-Systems University of Southern California, Los Angeles, CA 90089-2564 Phone: (213) 740-8386, Fax: (213) 740-4651 E-mail: [email protected], {jongwon,cckuo}@sipi.usc.edu

Abstract Transmission of continuous media such as video over time-varying wireless communication channels can benefit from the use of adaptation techniques in both source and channel coding. An adaptive feedback-based wireless video transmission scheme is investigated in this research with special emphasis on feedback-based adaptation. To be more specific, an interactive adaptive transmission scheme is developed by letting the receiver estimate the channel state information and send it back to the transmitter. By utilizing the feedback information, the transmitter is capable of adapting the level of protection by changing the flexible RCPC (rate-compatible punctured convolutional) code ratio depending on the instantaneous channel condition. The wireless channel is modeled as a fading channel, where the long-term and short-term fading effects are modeled as the log-normal fading and the Rayleigh flat fading, respectively. Then, its state (mainly the long-term fading portion) is tracked and predicted by using an adaptive LMS (least mean squares) algorithm. By utilizing the delayed feedback on the channel condition, the adaptation performance of the proposed scheme is first evaluated in terms of the error probability and the throughput. It is then extended to incorporate variable size packets of ITU-T H.263+ video with the error resilience option. Finally, the end-to-end performance of wireless video transmission is compared against several non-adaptive protection schemes. Keywords: Wireless video, fading channel, channel state estimation, forward error correction, and feedback-based adaptation.

I. INTRODUCTION Wireless multimedia services and products will soon become reality due to the advent of modern communication and information technologies and the rapid growth of the consumer market. Standards for third-generation (3G) wireless communication under the umbrella of IMT-2000 have targeted on the information rate up to 2 Mbps with limited mobility and 384 kbps at high-speed movement with seamless roaming. This implies the possibility of transmitting bandwidth-greedy video and other multimedia in a wireless environment in the near future. To provide a tangible solution for foreseeable wireless video applications, wireless video transmission has been intensively studied recently. It is well known that wireless channels tend to result in unreliable quality of transmitted media. The visual quality actually relies on several factors such as the speed of a mobile station, interference and so on. Hence, an adaptive transmission scheme is essential to achieve desired quality in a wireless multimedia communication system. In order to deal with time-varying characteristics of wireless channels, it should provide a dynamic adaptation capability in response to the channel status information, which eventually formulates an adaptive feedback-based system for rate adaptation, power control and handoff management. For feedback-based wireless rate adaptation, three kinds of adaptation such as adaptive modulation, adaptive forward error correction (FEC), and adaptive automatic repeat request (ARQ) are popular. Hybrid schemes of these have also been widely used to enhance end-to-end media quality. First of all, these adaptations rely on the estimated time-varying wireless channel condition, which can be captured as the simple received signal strength [1], or the extensive signal-to-interference plus noise ratio (SINR) [2]. That is, the accuracy of channel status estimation and the underlying time-scale of estimates are key

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factors of the adaptive paradigm. Also, reliability and timely availability (i.e., the associated feedback delay) of the channel information, which is dependent upon the characteristic of the feedback channel, is another key factor. However, until recently [3][4], ideal estimates of a fading channel in terms of shortterm SINR are usually undertaken in most existing adaptation works that have been centered at the optimized formulation of the joint source-channel coding framework [1][2]. In addition, reliable feedback with ideal no-delay is assumed in these approaches. In [3], an adaptive modulation under the outdated (i.e., delayed) channel estimates was studied. The adaptive modulation change per symbol in [2][3] is however impractical in the real-world situation because of the feedback delay and the high dynamic range of signals that may saturate the amplifier [4]. In [4], modulation has been adjusted per 260 symbol-width time-slot based on the maximum likelihood (ML) estimated SINR in order to maximize the overall throughput. Generally, the long-term fading reflects the averaged fluctuation of SINR of the short-term fading, which is averaged over a reasonable window. The level of averaged SINR may drop far below the expected level predicted from the path loss model, causing the adaptive modulation alone to lose the control of acceptable quality. Adding redundancy via FEC like RCPC (rate-compatible punctured convolutional) code helps to combat these worst-case errors and sustain the quality of multimedia. It can be further accompanied with the ARQ scheme as in [5], where RCPC is used to transmit image packets over a noisy channel. However, when dealing with a delay-sensitive video, retransmission is not allowed for most cases. Hence, only the adaptive FEC adaptation scheme is investigated at this stage to build a simple yet efficient rate adaptation for wireless video. Special attention has been paid to the accuracy, time-scale match, and delay of channel status estimation. In this work, the wireless channel is modeled as a fading channel, where the long-term fading is modeled as the log-normal function while the Rayleigh flat fading is assumed for the short-term. Considering the time-scale imposed by the deployed convolutional channel code (i.e., in the scale of the fixed-size channel frame), long-term fading parameters instead of short-term fading are tracked and predicted by using an adaptive LMS algorithm. The transmitter, by utilizing the feedback, is adapting the level of protection by changing the flexible RCPC code ratio for each channel frame. The performance of the proposed scheme is demonstrated in terms of the probability of error and the throughput. The advantage of the adaptive scheme over the non-adaptive one is evaluated for different feedback delays and different channel packet sizes. It is then extended to include packets of variable size of ITU-T H.263+ video with error resilience options. Finally, the end-to-end performance of wireless video transmission is compared against several non-adaptive protection schemes. The paper is organized as follows. The system model configuration and the video coder are described in Section II. We will explain the wireless channel model and the associated parameters used in the simulation framework in Section III. In addition, an algorithm for tracking and estimating long-term parameters, the deployed RCPC code, and an algorithm for adapting the channel code ratio are proposed. Section IV provides several experimental results of the proposed system starting from the bit level, passing through the dummy packet level, and finally reaching the video packet level. Concluding remarks and future works are given in Section V.

II. OVERALL SYSTEM CONFIGURATION AND VIDEO PACKETIZATION Parameters such as the bandwidth, reliability and delay of wireless channel are primary factors in the design of a wireless video system. Among the three mentioned above, the effect of bandwidth and reliability are mainly evaluated through ITU-T H.324/M-based wireless video setup shown in Fig. 1. For a wireless channel, the long-term and the short-term fading effects are modeled as log-normal and the Rayleigh flat fading, respectively. All channel symbols are subject to BPSK (binary phase shifted keying) modulation before sending through the wireless channel. The mobile station will decode the received signal via the ML-based scheme and estimate the long-term fading parameter. The calculated fading parameter will be fed back via a reliable channel to the base station to adjust the level of protection for

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upcoming streams. To be more specific, the level of protection is chosen based on the estimated averaged SNR level of the long-term effect as well as the target bit-error probability and the available channel bandwidth.

H.263+

MUX

Video Encoder

Adaptive FEC with RCPC

Wireless Channel (Rayleigh Flat Fading + Gaussian noise)

RCPC Channel Decoder with Long-term Fading Estimation

DEMUX

H.263+ Video Decoder

Clean Packet (Header/Payload OK)

Feedback Channel with delay

Corrupted Packet Fatal Packet

Figure 1: The wireless video system setup for the FEC-based rate adaptation via the channel state estimation and delayed feedback. For the integration with wireless video, a simplified version of multiplexing and de-multiplexing based on H.223 Annex B is derived and employed in our approach. It is aligned with the pseudo-MUX scheme except that only the video portion is transmitted and a fixed-size channel frame is finally utilized. The ITU-T H.263+ video with error resiliency and compression efficiency options (‘Annexes D, F, I, J, and T’ with random intra refresh of FI = 5) is used in the so-called ‘Anchor’ (i.e., GOB) mode [6]. That is, for every GOB (containing a fixed number of MB’s), a source video packet of variable size is formed. The variable-size video packet is then re-organized into the packet structure for each fixed-size channel frame as depicted in Fig. 2. Note that the fixed-size channel frame means that the total size of channel packet is fixed, which implies the payload and redundancy should be negotiated to maintain a fixed total. The header, synchronous field, and control field are strongly protected by using the BCH (Bose-ChaudhuriHocquenghem) code. The CRC (cyclic redundancy code check) is calculated for fields from the header to the payload and appended in order to allow the detection of bit corruption. The adaptive level of protection is then applied by using RCPC and added into the varying-size redundancy field. The output at the de-multiplexing stage is classified into three kinds of payload packets: clean, corrupted (CRC check failed packet) and fatal (unrecoverable error in header, synchronization or control field of multiplexing packet). Finally, to provide the end-to-end performance in both the subjective and objective measures, a decoder capable of handling the corrupted video stream performs video decoding.

Header

Synchronization

Control Field

Video Payload (t)

CRC

Redundancy (t) for RCPC FEC

Figure 2: The multiplexing packet structure for the adaptive FEC scheme.

III. BASELINE ADAPTIVE CHANNEL CODING SCHEME A. Wireless channel model The wireless channel model used in this paper for simulating real-world situation is shown in Table 1, where the transmitted signal is effected by short-term fading, long-term fading (shadowing) and path loss. The multi-path phenomenon generates the amplitude variation of transmitted channel signal, namely the SPIE Image and Visual Communications Processing `2000

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short-term fading. Path loss then reflects the degradation of the signal strength over a distance while fluctuations from the expected power level from the path loss model come from the shadowing effect. Table 1: Mathematical models of the simulated wireless channel. Mathematical models Path Loss

Parameters Pr: the received power in the free space

1 Pr = 2 Pt ( 4π d ) λ

Pt: the transmitted power d: the distance between base station and mobile station

Model

λ: the wavelength of electro magnetic wave propagation

Log-Normal:

f ( s) =

−( s − µ )2 2σ s2 e

1

σ s 2π

µ : the local mean of long-term fading fluctuation σs: the standard deviation of long-term fading in dB (σs can be varied from 4-12 dB depending on environment. In micro-cell, this varies from 4-7 dB with 5.5 dB as median. While in the macro-cell, it varies from 6.5-10.5 with median 9.3 dB [7][8].)

Spatial Correlation Model: Long-term Fading (Shadowing) Model

∆d d 2 R ( ∆d ) = σ s ρ d 0 0

ρ d : the normalized correlation at the distance d0 0 S(n): shadowing sample at time n

Density function at current time depending on the previous: f ( S (n) | S (n − 1)) =

( S ( n ) − ρS ( n −1)) 2

1

σ s 2π (1 − ρ 2 )

e

∆d / do : the normalized correlation ρ = ρ do

N(0,1): white Gaussian random variable (zero mean, unit variance)

2σ s2 (1− ρ 2 )

Relation of adjacent samples:

S ( n ) | S ( n − 1) = ρS ( n − 1) + σ s 1 − ρ 2 N (0,1)

Short-term Fading Model

U(r): the step function

Rayleigh fading channel: −πr

2

πr f ( r / s0 ) = 2 e 4 s U ( r ) 2 s0 2 0

S0 : the local mean of density function

In this work, all simulations have been conducted on an urban micro-cell wireless environment. The carrier frequency is set to fc = 2 GHz and the mobile station moves with a moderate velocity at 9.6 m/s or around 34 km/hr. Based on these assumptions, we can find the wavelength λ = 0.15m, from vc = fc λ, where vc is the speed of light. For simplicity, we also assume that power degradation regarding the path loss model is compensated perfectly to a given received signal strength, i.e., the SNR operation point. Generally, the long-term fading in a micro-cell has low to moderate deviation. The median value of the standard deviation is around 4 dB. We have chosen the standard deviation to be 4.3 dB as in [8]. The long-term fading is obtained by averaging out the effect of the short-term fading with a window size corresponding to 10λ. The essential simulation parameter in the shadowing (long-term fading) effect is the spatial correlation model, where a spatial correlation between two samples is modeled [8] as shown in Table 1. With the assumed velocity of a mobile station and the time-separation of 2 samples of the longterm fading to be 0.1565 sec (corresponding 10λ), we can get a spatial correlation value of 0.8348 to simulate the shadowing model. Regarding the Reyleigh short-term fading, with a moderate speed and the Doppler frequency at 64 Hz, we can calculate the normalized Doppler frequency. With the channel data rate of 64 kbps and the BPSK modulation, we can calculate the normalized Doppler frequency fd by fd = SPIE Image and Visual Communications Processing `2000

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Fd Ts, where Fd is the Doppler frequency and Ts is the symbol time. Thus, the normalized Doppler frequency used to construct the short-term fading is set to 10-3. Consider that the transmitted signal is BPSK-modulated before sending through the Rayleigh flat fading channel. The received signal at the decoder can be written as rk(t) = ak sk(t) + n(t), t ε [kTs, k(Ts+1)], where rk(t) is the received signal at channel decoder, ak is the amplitude variation of Rayleigh fading channel, sk(t) is the BPSK-modulated signal, and n(t) is the additive white Gaussian noise (AWGN). Assuming that we can estimate the short-term fading amplitude and that the BPSK signal has a unit power, after passing the correlator detection, the discrete channel model for symbol k is rk = ak sk + nk, where rk, sk, and nk are the corresponding discrete versions, respectively. Thus, the long-term fading parameter can be obtained by averaging the power of the short-term fading over a time window. For an Lsymbol window from k to k+L, the averaged power is PL = ( kk + L a 2 ) / L . The noise power PN can be k derived over the same L-symbol window as PN = ( kk + L ( r 2 − a 2 )) / L . Then, the long-term fading can be k

k

expressed by the ratio between PL and PN. This resulting variation of SNR at the ith window interval, S(i), is of the log-normal distribution characterized by the first-order Markov process.

B. Tracking and estimating channel status via long-term fading The variation process of the averaged power of the short-term fading, which gives the long term fading S(i), can be tracked by using the adaptive filtering technique as follows. The LMS algorithm is chosen in our approach because of its simplicity and performance. Weights of the FIR filter will be adapted to minimize the mean square error between the output and the reference signal. We can write the adaptive weight equation as W(n+1) = W(n) - 2µ ε(n) S(n), where µ is the stepsize controlling the convergence, W(n+1) = [ρ0(n), ρ1(n), ρ2(n), …., ρL(n)] is the set of adaptive filter coefficients, S(n) = [S(n), S(n-1), …, S(n-L)] is the set of previous channel estimates, and ε(n) = S(k+n) - WT(n+1) S(n) is the error from estimated value, respectively. We will use the first-order adaptive filter to track and estimate the current channel state. Hence, in the case of n-1 units feedback delay, the estimated SNR is Sˆ (k + n) = ρ o (n) S (n) , where Sˆ ( k + n) is the prediction value of the long-term fading at (k+n)th interval, ρ0(n) is the adaptive filter coefficient, S(n) is the long-term fading of nth interval at the decoder.

C. Rate-compatible punctured convolutional (RCPC) code RCPC [9] is the family of convolutional codes derived from a mother code rate 1/N, generator polynomials with memory M, and a puncture table. The rate can be changed by puncturing the bit at the encoder. The possible set C of code ratio is {P/(P+l), l = 1, …, (N-1)P}, where P is the puncturing period. Due to the flexibility in changing the code ratio, RCPC has been widely used in the unequal error protection and hybrid ARQ/FEC protection. That is, by changing the puncture table to obtain different ratio, the level of protection can be altered following the available feedback channel state. The Viterbi algorithm for MLSE (Maximum Likelihood Sequence Estimation) is used to decode data from a wireless channel with the puncture information. The performance in bit error rate (BER) for different code ratios for the simulated Rayleigh flat fading channel is shown in Fig.3.

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Figure 3: Performance of different ratio RCPC for the Rayleigh flat fading channel of Table 1.

D. Adaptive rate selection scheme to maximize throughput The use of an adaptive scheme based on the estimated channel state can increase the throughput at the receiver compared to the fixed scheme. The code ratio of RCPC is changed for every window size of shadowing effect (10-15 wavelength) in our proposed scheme. The feedback channel for channel state is assumed to be a reliable one with variable delay. Maximizing the expected value of throughput at a given SNR operation point with a set of RCPC codes is the goal to design an adaptive scheme under the constraint of a certain bit error probability denoted by Pbtarget. Let C ={c1, c2, c3, …, ck} be the code ratio set of RCPC codes. We assume that R(c1) > R(c2) > … > R(ck) (i.e., the level of protection increases with index ck). Based on the above discussion, we can formulate the problem as the maximization of the average throughput as Max Throughput (N) Ci ,i=0,1,…,N-1 where Throughput(N) is the overall throughput over N shadowing window transmission and Ci is the expected code rate of the ith-shadowing window. The rate selection scheme can be stated step by step as follows. Step 1: Initialize and estimate the channel state by using the long-term fading and the desired SNR range to choose the code ratio from the Pb-SNR table for maintaining Pbtarget Step 2: Use the outdated feedback channel status to choose the code ratio based on the set of region in Step 1, where the chosen rate will provide the highest throughput while maintaining its probability of bit error not exceeding Pbtarget in that region. Step 3: Calculate and predict the next channel state of the long-term fading and feed the information back to the encoder. We perform Steps 2 and 3 iteratively until the end of communication.

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IV. SIMULATION RESULTS The performance of the proposed scheme in terms of the probability of bit error and the throughput will be demonstrated first. For example, the advantage of the adaptive channel-coding scheme over the non-adaptive one is evaluated for different feedback delays and different channel packet sizes. Next, the proposed scheme is further extended to incorporate variable size packets of ITU-T H.263+ video with error resilience options. A simplified multiplexing version of ITU-T H.223 Annex B is implemented for this purpose, and the end-to-end performance of wireless video transmission is finally evaluated. For simulation parameters provided in Section III.A, Fig. 4 shows the adaptation of the proposed algorithm in terms of various levels of protection with respect to the long-term fading effect when the operational SNR is set at 18 dB. Each samples of long-term fading corresponds to 5 channel packets (or channel frames), where each packet has a size of about 2000 bits (an average of the short-term fading effect over a distance of around 10λ). Fig. 4 shows that the system provides low (or high) protection when channel is bad (or good). Other experiments of the proposed adaptive algorithm will be presented in 3 levels composing of bit level, dummy packet level and H.263+ video level as detailed below.

Figure 4: Change of RCPC code ratio according to the estimated long-term variation. A.

Bit Level

The result of the bit error probability for the proposed adaptive scheme is shown in Fig. 5. Three cases (i.e. no delay, 1-unit delay and 2-unit delay) are compared with different non-adaptive systems. Here, we set the target bit error probability at 10-3. As shown in Fig. 5, we see that the adaptive system can provide a nearly constant bit error probability when the SNR operational point changes along the time. When the feedback channel has some delay, the bit error probability can still maintain at a certain value that is slightly higher than that of the no-delay case.

B. Dummy Packet Level The transmitted packet structure is shown in Fig.2. In our simulation, the payload from a clean packet will be used to calculate the throughput while corrupted and fatal packets will be discarded. Experimental SPIE Image and Visual Communications Processing `2000

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results comparing the throughput between the proposed adaptive system with different delays and the non-adaptive system are shown in Fig. 6, where there exists a shadowing variation of 4.3 dB. We see that the adaptive system provides an improved throughput than the non-adaptive one at the same SNR operational point.

Figure 5: The probability of bit error under constraints of 1) the target probability of bit error less than10-3, 2) 4.3 dB shadowing variation, and 3) no, 1-unit and 2-unit delays. Even for the delayed feedback case, the proposed adaptive system is superior to every fixed-rate RCPC system. The overhead of different packet sizes of the adaptive algorithm with no delay and 4.3 dB variation is shown in Fig. 7 with a packet size of 1000, 2000 and 3000 bits/packet. The comparison in terms of the percentage of the number of clean packet and the throughput for different packet sizes is given in Fig. 8.

C. Video Level To evaluate the performance of the proposed adaptive FEC transmission, 4000 frames (circulated) of QCIF “Silence Voice” sequence is used. We compare the averaged PSNR of frames of the proposed adaptive and the non-adaptive systems. To achieve a fair comparison, we attempt to match the utilized channel bits of both schemes as close as possible. The result of the averaged PSNR with a target probability of bit error equal to 10-3 with respect to different channel conditions is shown in Table 2. We see that the adaptive system can provide an averaged PSNR, which is as close or even higher than the non-adaptive system while using fewer channel bytes under various simulated channel conditions.

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Figure 6: Comparison of the throughput of the proposed adaptive system of various delayed feedback and several non-adaptive systems with 2000 bits/packet and 4.3 dB shadowing variation.

Figure 7: Overhead of variable-size packets for the adaptive system with 4.3 dB shadowing variation.

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6000 Throughput (Byte/s)

Percentage of clean packet

7000

100 90 80 70 60 50 40 30 20 10 0

1

5000 4000 3000 2000 1000 0

0

2

4

6

8

10 12 14 16 18

0

2

SNR operation point (dB) 1000 bits/packet

2000 bits/packet

4

6

8

10 12 14 16 18

SNR operation point (dB)

3000 bits/packet

1000 bits/packet

2000 bits/packet

3000 bits/packet

Figure 8: Performance comparison of the percentage of clean packets (left) and the throughput of varying packet sizes for no delay adaptive-system with 4.3 dB shadowing variation (right). Table 2: Comparison of the average PSNR performance for the Silence Voice Seq. By using the proposed adaptive system with Pb = 10-3 and the fixed convolutional code rate system.

Adaptive Fixed convolutional code Channel Algorithm rate Condition Channel Y Channel Y Code byte PSNR byte PSNR rate (dB) used (dB) used (dB) (bytes) (bytes) 6 dB 8 dB 10 dB 12 dB 14 dB 16 dB 18 dB

3355500 2990250 2702750 2475250 2292500 2140750 2016250

25.88 26.82 27.55 28.24 28.54 28.80 28.99

3364500 2987750 2502250 2502250 2502250 2107250 2107250

25.43 26.20 27.02 28.21 29.03 28.39 29.16

1/2 4/7 2/3 2/3 2/3 4/5 4/5

Difference in Channel Byte used (%) -0.2675 0.0837 8.01 -1.08 -8.38 1.59 -4.3187

V. CONCLUSION AND FUTURE WORK The wireless channel is a hostile channel. It produces a high probability of bit error due to the effect of path loss, shadowing and multipath fading. These effects limit the capability in transmitting multimedia. The use of known channel state information will help to use the resource in a wireless system more effectively. Different degrees of protection with RCPC were used in this work to adjust the level of

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protection with respect to the time varying channel. The long-term fading parameter is chosen to select the level of protection at the encoder instead of the short-term fading since changing the channel coding too frequently may not be possible in real practice. An adaptive filtering technique, i.e. the LMS algorithm, was used to estimate the long-term fading parameter. The multimedia data considered in this paper was the H.263+ video standard. Experimental results were demonstrated in the form of rate change, throughput, the percentage of clean packet over the corrupted packet, and the PSNR value of video. The effect of delayed feedback was also considered. We are currently studying the integration of unequal protection of media content and the adaptive RCPC coding schemes based on the channel status estimation/feedback, and expect to get a better result of the decoded video.

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