Impact Of ATM Traffic Shaping On MPEG-2 Video Quality*

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1 IJCA, Vol. 10, No. 3, Sept Impact Of ATM Traffic Shaping On MPEG-2 Video Quality* Yongdong Wang and Michael Jurczyk University of Missouri - Columbia, Columbia, Missouri 65211, USA Abstract This paper presents the impact of traffic shaping on the quality of MPEG-2 video transmissions over ATM networks. The performance evaluation is accomplished by simulating an ATM network using real MPEG2 video streams from video broadcast applications. First, it is shown how cell loss influences the perceived video quality. Cell loss can result in either frame corruption or frame loss, which causes differing video quality impairments. Next, a simulation study is conducted showing how leaky-bucket traffic-shaping influences video quality. It is discussed how shaper parameters have to be chosen to obtain highest video quality. In general, underdimensioning as well as overdimensioning of the shaper should be avoided because it results in video quality degradation. Also, it is shown that the optimal choice of the shaper s token buffer size depends on the overall motion/burstiness in the video. A well-dimensioned shaper is able to increase video quality significantly as compared to not shaping at all. Most of the research on traffic shaping centers on the characteristic of the shaped traffic rather than on the resulting application performance. This study clearly shows that traffic shaping can actually enhance video quality significantly while it shapes the traffic to adhere to the negotiated QoS (Quality of Service) parameters. Key Words : ATM networks, networked multimedia, MPEG video coding, traffic shaping, video quality 1 Introduction Digital video transmission is a difficult and challenging issue in the area of communication. The bandwidth requirements for transmitting digital video are several orders of magnitude greater than for regular data transmission [25]. This drives the evolution of Broadband Integrated Service Digital Networks (B-ISDN) [6]. The underlying technology that makes B-ISDN possible is the Asynchronous Transfer Mode (ATM) technology. Due to its high bandwidth and QoS * This research was supported by the University of Missouri Research Board under Grant RB Department of Computer Engineering and Computer Science, 121 Engineering Building West, mjurczyk@cecs.missouri.edu. guarantees, ATM networks are able to provide high-quality digital video transmission [16, 23]. Numerous works have been reported about video transmission over ATM networks. It is possible to control the video coding procedure and generate a constant bit rate (CBR) video stream [10], which can be transmitted efficiently using ATM s AAL-1. However, it results in varying video quality. Videos can also be encoded using fixed quantization (fixed video quality), resulting in variable bit rate (VBR) video streams [20]. On the one hand, when several independent VBR video streams share the same ATM switch buffers, the low bit rate of one source will be offset by the high bit rate of other sources, resulting in high network utilization. On the other hand, buffer overflow might occur if multiple sources transmit at high rate at the same time, resulting in cell loss. Due to the MPEG coding, losing a data unit can cause information loss not only in the current frame but also in the dependent frames. In addition, VBR video exhibits significant burstiness due to scene changes and the compression algorithm. These properties make the multiplexed VBR video transmission over ATM a challenging issue. To combat the cell loss problem, control mechanisms are present in ATM networks: (1) connection setup negotiation and (2) traffic shaping/policing. During connection setup negotiation, traffic descriptors (peak and average cell rate, burst length and duration) and service requirements (maximum tolerable delay and desired delay jitter) are taken into account to determine whether to accept the connection and the amount of resources to reserve for a particular connection. The second method is to use traffic shaping/policing to change the behavior of the VBR video stream. Numerous works about traffic shaping/policing were recently introduced in the literature, e.g., [1, 2, 5, 7, 12, 14, 18, 19, 26-28]. A traffic policer sits at the network side and acts like a policeman to prevent the end-users from breaking the QoS contracts. If the measured traffic exceeds the negotiated parameters, the traffic policer will drop cells, or mark them as being low priority [1, 12, 14, 26]. Traffic shaping is done at the user side. The user traffic is shaped by delaying cells rather than dropping cells to help meet the QoS contract [18, 19]. While it was shown in [12] that the choice of the traffic policing algorithm has a profound impact on the video quality, the influence of traffic shaping on video quality is still not well studied (most of the research on traffic shaping centers on the ISCA Copyright 2003

2 2 IJCA, Vol. 10, N0. 3 Sept characteristic of the shaped traffic rather than on the resulting application performance). This paper therefore studies the influence of traffic shaping on video quality. It is shown that leaky bucket traffic shaping is able to increase broadcast video quality significantly without breaking the QoS contract. The paper makes the three following contributions: 1) The perceptual impact of cell loss on video quality is discussed. Cell loss can result in either frame corruption or frame loss, which causes differing video quality impairments. 2) The influence of traffic shaping on the resulting application performance and not just on the characteristic of the shaped traffic itself is studied. It is shown that traffic shaping, which is normally used to shape the traffic to adhere to the negotiated QoS parameters, can also help to achieve higher video quality. 3) It is shown what kind of tradeoffs are involved in choosing optimal shaper parameters and how these parameters can be calculated from the video statistics. The rest of the paper is structured as follows. In the next section, MPEG coding is reviewed, while in Section 3, ATM basics and the leaky bucket traffic shaper are presented. In Section 4, the perceptual impact of cell loss on video quality is discussed. The simulation setup is introduced in Section 5. Section 6 presents a simulation study on how traffic shaping influences the quality of video transmitted over ATM networks, while optimum shaper parameters are derived in Section 7. Conclusions are presented in Section 8. 2 MPEG Video Coding The acronym MPEG stands for Moving Picture Expert Group, which worked to generate the specification under ISO [11]. The specification includes three standards: MPEG-1, MPEG-2, and MPEG-4. The MPEG-1 and MPEG-2 standards are similar in basic concepts. Both of them are based on motion compensation and DCT (Discrete Cosine Transform) coding. MPEG-2 supports several profiles and resolution levels. MPEG-4 deviates from these traditional approaches and introduces the notion of objects that can be coded and transmitted independently. Because MPEG-2 is a finalized standard and is presently being utilized in more and more applications, this study concentrates on MPEG-2 video. MPEG video is composed of a hierarchy of layers, which are used for error controlling, random search, and synchronization. From the top, the first layer is the video sequence layer. The second layer is the group of picture (GOP) layer, which is composed of one intraframe (I frame) and some nonintraframes (P or B frames). The third layer is the picture layer and the layer beneath is the slice layer. Each slice is a contiguous sequence of macroblocks (typically, a slice is a picture row). Macroblocks can be further divided into blocks, which are 8 8 arrays of pixels. Each of these layers has it s own unique 32-bit start code. As in the standard for still image compression, Discrete Cosine Transform (DCT) is used in MPEG coding. DCT decomposes the signal into underlying spatial frequencies. It applies to each 8 8 block. A frequency-adaptive quantization of the DCT coefficients, according to human visual characteristics, is performed. As the result of DCT and quantization, most of the higher frequency coefficients have been quantized to zero. Since most of the non-zero DCT coefficients are typically concentrated in the upper left hand corner of the coefficient matrix, a zigzag scanning pattern together with run-length coding of the DCT coefficients is used to optimize compression. As a final step, Huffman-like entropy coding is applied. Considerable compression efficiency can be achieved if temporal redundancy is considered. Temporal processing exploiting this redundancy uses motion compensation prediction that is applied to each macroblock. The principle of motion compensation prediction is that typically consecutive video frames are similar. A two -dimensional spatial search is performed on each luminance macroblock of the referenced frame. When a relatively good match with the current macroblock is found, the encoder assigns a motion vector to the macroblock. After obtaining the motion vector, the difference (residual error) between the current and the referenced macroblock will be coded and appended to the motion vector. If no match is found, the current macroblock is coded using the intraframe-coding technique. A video frame can be encoded as an intra-frame (I frame), forward-predicted frame (P frame), or bi-directional predicted frame (B frame). An I frame is encoded as a single image with no reference to any past or future frames, while motion compensation is used in B and P frames. In P frames only forward prediction is used, while B frames are coded based on a backward prediction from succeeding I or P frames, as well as on a forward prediction from a previous frame. Backward prediction requires the future frame to be encoded and transmitted first. Each video sequence is composed of a series of Groups of Pictures (GOPs). The GOP structure is intended to assist random access into a sequence. A GOP is an independent decodable unit that can be of any size as long as it begins with an I frame. Figure 1 shows a GOP pattern in which the arrows represent the inter-frame dependencies. A typical, widely used GOP is the sequence IBBPBBPBBPBB, which is also used in this study. Figure 1: A GOP encoding pattern and dependencies. 3 ATM Networks An ATM network is a cell-switching network that uses a fixed length packet (cell) as the transmission unit [16]. The length of a cell is 53 bytes with a 5 byte header and a 48 byte payload. ATM networks operate in a connection-oriented

3 IJCA, Vol. 10, No. 3, Sept mode: first, a logical/virtual connection is setup, over which data is then transferred. During the connection setup phase, quality of service (QoS) parameters such as sustained cell rate, peak cell rate, and burst length can be defined for a specific connection. The call admission control (CAC) then tries to setup the connection adhering to these QoS parameters. Once the connection is up, these parameters are guaranteed by the network for the lifetime of the connection. The core of an ATM network consists of ATM switches interconnected by fiber links. An ATM switch consists of a switch fabric and cell buffers that are used to eliminate cell contention. Three main switch architectures exist that mainly differ in the placement of the internal cell buffers [3, 4, 13]. In input-buffered switches, a buffer is located at each switch input port; in output-buffered switches, a buffer is located at each switch output port, while in central-memory buffered switches, cells are buffered in a central memory. Output queueing is widely used and the ATM switch used in this study is based on this buffering strategy. Symmetric output-buffered switches with B inputs and B outputs are assumed, in which queues are located at each output port of the switching element. Cells arriving simultaneously at input ports that are destined for the same output are queued in the buffer of that output port. The switch is able to write up to B cells (at most one cell from each input) to a specific output queue during one switch cycle time to avoid cell loss. The cells in the output queue are served on a FIFO basis to maintain the integrity of the cell sequence. Cells will be dropped if they reach a full output buffer. Many different traffic shapers were introduced in the literature. In contrast to traffic policers that discard nonconforming cells, traffic shapers space cell departures by delaying cells in a buffer. To accomplish this, a leaky bucket is most often used. The general model of a leaky bucket traffic shaper is depicted in Figure 2 [14, 17, 24]. The Leaky Bucket consists of an input buffer of size IB and a token buffer of size TB. Cells drain out of the input buffer with a rate of IBR and tokens drain out of the token buffer with a rate of TBR. To guarantee that no cells are lost in the shaper, the input buffer size is chosen very large. TB and TBR can be adjusted, while IBR is set to be IBR=TB/T + TBR with T the frame duration of 33 ms [14]. Every cell entering the network (leaving the input buffer) places one token into the token buffer. If the token buffer is full, cells will wait in the input buffer until an empty slot in the token buffer is available. 4 Perceptual Impact of Cell Loss on Video Quality Losing information in an MPEG stream results in video quality degradation. The level of degradation depends on what information is lost and to which frame the information belongs. Information loss results in two distinct video impairments, depending on whether information within a frame or a frame itself is lost. 4.1 Loss of Information within a Frame The loss of information within a frame will impact the frame slice the lost information belongs to. As an example, consider Figure 3. Figure 3a shows an original I-frame of the flower garden movie, while Figure 3b shows the decoded frame when losing one ATM cell. The video sequence was encoded with a video slice corresponding to a horizontal strip of an image. The loss of a cell causes image corruption up to the next resynchronization point (i.e., the next slice header). This is referred to as spatial loss propagation. The extent of frame corruption depends on the relative position of the lost information within a slice. Due to the predictive nature of the MPEG-2 algorithm, when losses occur in a reference frame (I or P), image corruption will remain until the next resynchronization point (i.e., the next I frame) is received. This results in the impairment propagation across multiple frames, which is known as temporal loss propagation. This is shown in Figure 4 where the original last B-frame of the GOP (Figure 4a and the corrupted last B-frame of the GOP (Figure 4b are shown, assuming cell loss in the GOP s I-frame. Figures 3b and 4b also show that the MPEG decoder used implicitly implements some error concealment. Without any error concealment, the depicted cell loss would result in a black block starting from the point of cell loss and ending at the splice end (cell loss and spatial loss propagation in Figures 3b) and 4b) [8]). This happens if after a frame was decoded, the frame buffer space in the decoder is reset to all zeros. When the next frame is decoded into this buffer and part of a splice is lost, the corresponding zeros will not be overwritten, resulting in the black block. In the decoder used, buffer space is not reset before a new frame is decoded. Thus, when part of a splice is lost, the corresponding buffer section is not overwritten and will contain decoded macroblocks from the previous frame, alleviating the impact of cell loss on video quality. 4.2 Loss of Frame Figure 2: General model of a leaky bucket Each encoded frame in an MPEG stream starts with a START_OF_PICTURE (SOP) code. When processing a bit stream, the decoder will search the stream until an SOP occurs. After receiving the SOP the decoder starts to decode the

4 4 IJCA, Vol. 10, N0. 3 Sept information that follows to assemble the frame. If an SOP is lost during stream transmission, the decoder re-synchronizes by reading in the bit stream and discarding the bits until it recognizes the SOP of the next frame in the stream. During this resynchronization, the decoder will continue displaying the previous frame. Thus, when an SOP is lost, the corresponding frame will be skipped and a previous frame will be displayed instead. The effect of frame loss on video quality depends on the type of frame lost. Because no other frames depend on B frames, no frames will be corrupted if a B frame is lost. However, because a different frame will be displayed, a slight jerk that depends on the overall motion in that scene might be visible when watching the movie. When an I or P frame is lost, all P and B frames that depend on the lost frame will most likely be corrupted. Motion vectors in the P and B frames point to macroblocks in the lost frame. The decoder uses the corresponding macroblocks from the previous I or P frame instead, which results in the corruption of moving objects within the frame. Because other P and B frames within the GOP depend on the corrupted frame, those frames will be corrupted as well (temporal loss propagation). The decoder will resynchronize after the fetch of the next I frame, ending the video corruption. This impairment is shown in Figure 3c. In the scene the tree is a moving object (moving from right to left through the picture), while the background is moving only very slowly. In Figure 3c, the decoded B frame following a lost I frame is shown. The moving object (tree) is corrupted and although not really visible in Figure 3c, parts of the background are corrupted as well. Figure 4c shows the last B- frame of the GOP that is also corrupted because of the I-frame loss (temporal loss propagation). In this paper, video quality is judged by the average mean square error MSE (calculated by comparing each displayed frame with its corresponding original fra me) over all displayed video frames. Because the loss of I and P frames results in the corruption of dependent P and B frames in a GOP, frame loss and MSE are correlated. However, this is not true if a B frame is lost (a previous frame will be displayed instead, resulting in the difference of only one frame), so that the percentage of frame loss is also used as a video quality measure. The quality of the corrupted videos was also judged subjectively by watching the video. A subjectively lower video quality coincided with a higher measured MSE, as will be shown further in Section 6. For example, the video quality resulting from losing a frame (Figures 3c and 4c) was judged as being lower than the video quality resulting from losing a cell within a frame (Figures 3b and 4b). This coincided with an MSE = 400 for the lost frame case and an MSE = 20 for the lost cell case (measured over the affected GOP). 5 Simulation Setup In our study, the system under consideration shown in Figure 5 is used. It includes an ATM switch, a number of lost cell spatial loss propagation (a) (b) (c) Figure 3: (a) original I frame, (b) effect of cell loss within I frame, (c) effect in following B frame when I frame is lost (a) (b) (c) Figure 4: (a) original B frame, (b) effect of cell loss within I frame, (c) effect of loss of I frame; last frame (B) of GOP shown

5 IJCA, Vol. 10, No. 3, Sept video sources, traffic shapers, and a destination. In this study, we consider video broadcasting applications. Video broadcasting has a maximum end-to-end delay requirement of 500ms [9] (including encoding, cell packaging, transporting, decoding, and displaying of frames). Cells received by the destination that would result in a higher end-to-end delay will be dropped by the destination. Figure 5: ATM system diagram Eight different real MPEG-2 video clips are used as the video sources for video broadcasting applications. The movies used were downloaded from different web sites including [15] and converted to MPEG-2. Essential simulation results for all eight movies are shown in Section 7, while in-depth results for three of those eight movies are shown in Section 6. The first of these three videos is a low motion video clip that is part of an interview. It is approximately 42 seconds long (consisting of 1,297 frames with a size of pixels) with all low motion scenes. In the following discussions, we call this video clip low motion video. The second video is a movie clip from the trailer of the movie Titanic. It is approximately 45 seconds long (consisting of 1,408 frames with a size of pixels) with a mix of high and medium motion scenes. In the following discussions, we call this video clip medium motion video. The third movie depicts an animated high-speed flight over a surface. It is approximately 25 seconds long (consisting of 733 frames with a size of pixels) with high motion scenes and is therefore called high motion video. All three video clips were encoded with the group of picture (GOP) pattern IBBPBBPBBPBB. Video can be characterized by, among others, its average cell rate (ACR), which is defined as the total number of ATM cells in the video clip divided by the playing time of the video. The low motion video clip used in this study has an ACR of 2,640 cells, the medium motion video clip has an ACR of 4,800 cells, while the high motion video clip has an ACR of 5,310 cells. Frame statistics of the three movies are listed in Table 1. For transmission, the video stream is packaged into AAL-1 layer cells with a 47-byte payload. The number of video sources in a simulation under consideration ranges from 10 to 16 to make a reasonable traffic load range. The start time of each video source is randomly selected within 360 ms (the time period of one GOP). Our ATM simulator is based on the ATM simulator developed by the (National Institute of Standards and Technology (NIST) [22] and was adapted to incorporate real video sources and the various traffic shapers discussed in this study. All simulation results shown are the average of 60 independent simulation runs. All ATM switches studied have output buffers with a length of 100 cells each. For the low motion video simulations, the switch speed is set to 25 Mb/s and the switch is connected to 25 Mb/s physical links with a length of 1 km each. For the medium and high motion video simulations, the switch speed is set to 50 Mb/s and the switch is connected to 50 Mb/s physical links with a length of 1 km each. These rather low switch/link rates were chosen to obtain a reasonable simulation run time (less video sources have to be used to generate realistic switch loads). Longer simulation runs with faster switches/links and more video sources show that the results presented and conclusions drawn are also valid for higher switch and link rates (e.g., OC-3). The MPEG2 encoder/decoder pair we used is the MPEG-2 Video Codec developed by the MPEG Software Simulation Group (MSSG) [21]. If the frame -start-code of a frame is lost during the transmitting, the decoder will not decode the frame but skip it. Thus, frames might be lost. If other parts of a frame are lost, the decoder is able to decode that frame but will introduce errors in that frame. To capture the quality of a transmitted video, we used three measures: (1) the cell loss ratio (number of lost cells divided by the number of all cells transmitted), (2) the frame loss ratio (number of lost frames divided by the number of all frames), and (3) the mean square error (MSE) of the received frames that are compared to their corresponding original frames. During a simulation run, a sending node will send the MPEG-coded video file frame -by-frame to the shaper/network with frame duration of 33ms. An MPEG-coded video frame i to be sent out is divided into n i ATM cells; the cells are equally spaced during the frame period of 33ms, and are sent out. At the receiver side, received video data is combined into an mpeg video file. Video data lost in the network is therefore not included in the reconstructed MPEG video file. The received video file is then decoded into individual UYV frame components using the MPEG decoder. The decoder was slightly modified to indicate frames that were skipped during the decoding process to calculate the lost-frame-ratio. Each U, Y, and V frame component is then compared to its original Table 1: Minimum, maximum, and average number of cells per frame of the movies used Low motion movie Medium motion movie High motion movie Frame Type I B P I B P I B P Min Max Average

6 6 IJCA, Vol. 10, N0. 3 Sept (non-corrupted) counterpart to calculate the resulting MSE. 6 Effect of Regular Traffic Shaping on Broadcast Video Quality In this section we investigate the effects of traffic shaping on the quality of broadcast video, which is not very delay sensitive and allows a maximum end-to-end delay of 500ms. The influence of the traffic shaper s TB and TBR on the video quality is studied. TB adjusts the burst size and peak cell rate of the cell stream. TBR adjusts the sustained cell rate (SCR) of the cell stream leaving the shaper and is therefore related to the ACR of the video clip. In all simulations of this study, we set TBR = S*ACR with S being an overdimensioning factor with 1 S 5 [14] that is used to scale the TBR (note that when S is larger than 5, the traffic shaper loses its effect and the video transmission becomes VBR). The overdimensioning S determines the rate at which the video stream is released into the network. If a large factor (S > 5) is chosen, arriving cells will be instantaneously sent into the network without any shaping. The smaller S is, the longer cells have to wait in the input buffer to be released into the network. First we investigate the effect of TBR on the video quality. Sixteen video sources were used to generate a relatively high network load. The token buffer size was set to TB = 100 cells. Figures 6, 7, and 8 show the resulting cell loss ratio, frame loss ratio, and MSE for the low motion movie (Figure 6), the medium motion movie (Figure 7), and the high motion movie (Figure 8). It can be seen that if TBR is smaller than ACR (S < 1), the video quality is the worst (high frame loss and MSE). More and more cells will pile up in the input buffer of the leaky bucket traffic shaper, which results in high cell delay so cells cannot meet the end-to-end delay requirement. If TBR is higher than ACR (S > 1), video quality (frame loss and MSE) begins to degrade with increasing S. Especially in the range of 1.4 S 2.4, the video quality degrades sharply. From those curves, it can be seen that the degradation is coming from the increased cell loss rate. It turns out that for TBR > ACR, the higher the Sustained Cell Rate, the higher the cell loss rate and the lower the video quality. The study shows that, for S > 1, all cells are dropped in the switch because of congestion in the network, while no cells are dropped for exceeding the end-toend delay. This shows that the dominant factor of cell loss for video broadcasting application is network congestion. When the traffic is heavily overdimensioned (S = 3.0), the video quality degrades little and becomes stable with the increasing S. That is because for S > 3.0, the video source traffic resembles VBR more closely so that the further increase of S has little impact on video quality. Furthermore, with increasing S, both frame loss rate and MSE increase. For 1.4 S 5, approximately 2 percent of the lost frames are I-frames, 22 percent are P-frames, and 75 percent are B-frames. Thus, around 25 percent of the lost frames are reference frames, and the loss of these frames will result in a higher MSE because of the temporal loss propagation as discussed in Section 4. Therefore, the increase in MSE whenincreasing S stems from cell loss within frames and from reference frame loss. To investigate the effect of the token buffer size TB on video broadcast performance, we select S = 1.0, 1.2, and 1.4, under which relatively high video quality is gained (see Figures 6 to 8), and vary TB. The results are shown in Figures 9 to 11 for the low, medium, and high mo tion video cases. Highest video quality (lowest frame loss rate and MSE) is achieved with a token buffer size of around 100 for the low motion, about 50 for the medium motion movie, and around 10 for the high motion movie. For the low motion video, the size of I frames is much larger than the size of P frame and B frame as compared to the medium and high motion video cases (see Table 1). The lower the motion in a video, the more effective the motion compensation mechanism producing smaller B and P frames. The low motion movie is therefore burstier than the medium motion video, which in turn is burstier than the high-motion video. To account for this varying burstiness, the token buffer size has to be increased for decreasing motion in a video to increase the burstiness of the traffic stream leaving the shaper. Figures 12 to 14 show a comparison of video broadcasting performance with shaping and without shaping (VBR) under different network loads (number of video sources). It can be concluded that a leaky bucket traffic shaper with TBR and TB chosen properly improves the broadcast video quality significantly, while it is able to shape the traffic to conform to the QoS contract. How to properly choose TB and TBR is further discussed in Section 7. To further illustrate the gain in video quality through traffic shaping, Figures 15 and 16 depict (a) the original frames, (b) received and decoded frames under the traffic scenario of 16 VBR video sources, and (c) received and decoded frames under the traffic scenario of 16 shaped video sources (with TB = 10, and S = 1.0) from the high-motion movie. In both the VBR and shaped traffic cases, the simulations were run with the same random number generation seed resulting in the same start times of the movies between the two simulations (while the start times of the 16 movies belonging to a specific simulation run differed). Thus, the only difference between the two simulations was the traffic -shaping algorithm used (either shaping or no shaping). In many cases, while frames in the VBR traffic case were highly distorted, the corresponding frames in the shaped traffic case were free of distortion. To consider a worst-case scenario, frames were selected in Figures 15 and 16 that had high distortion in the shaped traffic case. Figure 14 suggests a 20-fold reduction in MSE when using shaping. This is also evident from the actual decoded frames. While in the VBR case the frames shown have unacceptable quality, the frames in the shaping case show much less, more acceptable distortion. The findings can be summarized as follows. For video broadcasting applications, the dominant factor leading to cell loss is network congestion, not the end-to-end delay. To gain the best video broadcasting performance, TBR should be set toacr (S = 1; i.e., the video stream sustained cell rate is equal to the average cell rate of the movie), which largely reduces the network congestion and lowers the cell loss rate. The optimal TB depends on the motion/burstiness of the video. In

7 IJCA, Vol. 10, No. 3, Sept Figure 6: Low motion video broadcasting performance under varying TBR, 16 video sources, TB = 100, TBR = ACR * S Figure 7:Medium motion video broadcasting performance under varying TBR, 16 video sources TB = 100, TBR = ACR * S Figure 8: High motion video broadcasting performance under varying TBR, 16 video sources, TB = 100, TBR = ACR * S our scenarios, a TB of around 10 is suitable for the high motion video, a TB of around 50 is suitable for the medium motion video, while a TB of around 100 is suitable for low motion video. With those reasonably selected TBR and TB, the leaky bucket traffic shaper significantly improves the video broadcasting performance (decrease in the frame loss by a factor of up to 75; decrease of MSE by a factor of up to 65) compared with direct VBR video broadcasting. This results in a substantial increase in perceived video quality. 7 Leaky Bucket Shaper Dimensioning In the previous section it was shown that for reasonably selected TBR and TB, the leaky bucket traffic shaper significantly improved the video broadcasting performance. In this section, how to calculate the optimum token buffer rate and size is studied. A multimedia system is assumed where movies stored on a video server are streamed out to users. In this case, characteristics of a video such as average cell rate and burstiness can be calculated off-line and these parameters can be used to dimension its associated traffic shaper before the video is streamed out. As was shown in the previous section, the token buffer rate TBR of the leaky bucket traffic shaper should be set to the average cell rate (averaged over all frames) of the movie

8 8 IJCA, Vol. 10, N0. 3 Sept Figure 9: Low motion video broadcasting performance under varying TB and S, 16 video sources, TBR = ACR * S Figure 10: Medium motion video broadcasting performance under varying TB and S, 16 video sources, TBR = ACR * S Figure 11: High motion video broadcasting performance under varying TB and S, 16 video sources, TBR = ACR * S (S = 1.0). Assuming a movie with N frames and assuming that the size (in cells) of a frame i is c i, TBR (in cells per frame) can be calculated to: TBR = ACR = N i= 1 N c i. (1) The optimum token buffer size TB depends on the burstiness of the movie, as was shown in the previous section. There are several ways to specify the burstiness of a movie. One way is to compare the average size of I frames to the average size of B frames. Because the cells belonging to a frame are assumed to be spaced evenly over time over a frame duration, a burst change occurs only at every frame change. This burst change will be the highest if a frame consisting of a small number of cells is followed by a frame consisting of a large number of cells and vice versa, which normally happens when an I frame is transmitted following a B frame and vice versa. Comparing the average I frame size to the average B frame size will therefore give an indication on the burstiness of the movie. This characteristic is used here to calculate the optimum token buffer size TB. Assuming a movie with an average I frame size of I a and an average B frame size of B a, the following

9 IJCA, Vol. 10, No. 3, Sept Figure 12: Effect of traffic shaping on low motion video broadcasting, TB=100, S=1.0 Figure 13: Effect of traffic shaping on medium motion video broadcasting, TB=50, S=1.0 Figure 14: Effect of traffic shaping on high motion video broadcasting, TB=10, S=1.0 simple approximation is used to calculate the optimum TB (in tokens) TB I a = F Ba with F being a constant. The constant F was determined empirically by using the aforementioned eight movies of different sizes and content/motion as listed in Table 2, and (2) obtaining the optimum TB through simulation. Simulations were run with 16 video sources and with TB in the range from 0 to 200 tokens with an increment of 10 tokens. It was found that a good approximation was achieved when choosing F = 8 tokens. Optimum simulated TB and calculated TB using Equation (2) with F = 8 for these movies are shown in Table 2. Equation (2) tracks the optimum simulated token buffer size quite well. It has to be noted though that Equation (2) is only an approximation and a more rigorous study on how to calculate an optimum TB is needed to derive a more accurate optimum token buffer size.

10 10 IJCA, Vol. 10, N0. 3 Sept (a) (b) (c) Figure 15: Effect of traffic shaping on video quality (a) original frame, (b) received frame without shaping (VBR), (c) received frame with shaping (a) (b) (c) Figure 16: Effect of traffic shaping on video quality (a) original frame, (b) received frame without shaping (VBR), (c) received frame with shaping Table 2: Simulated and calculated optimum TB for movies under investigation (F = 8) Movie Frame size (in pixel pixel) Average I frame size (in cells) Average B frame size cells) Simulated optimum TB (in cells) Calculated optimum TB (in cells) Low motion movie Medium motion movie High motion movie Quelle DH Storm Epson Mystic Conclusions The influence of traffic shaping on the quality of broadcast video transmitted over ATM networks was studied. First, the perceptual impact of cell loss on video quality was discussed. It was shown how losing cells within a frame and losing a frame s start code influences video quality. Then the effects of a leaky bucket traffic shaper on video quality was studied under different token buffer rates and token buffer sizes, and the tradeoffs involved were discussed. In general, underdimensioning of the shaper (i.e., injecting cells into the network with an average rate lower than the average video cell rate) as well as overdimensioning of the shaper (i.e., injecting cells into the network with an average rate higher than the average video cell rate) should be avoided because it results in video quality degradation. It was also shown that the optimal choice of the shaper s token buffer size depends on the overall motion/burstiness of the video. It was studied how the optimum token buffer size can be approximated from video characteristics such as average frame sizes. Furthermore, it

11 IJCA, Vol. 10, No. 3, Sept was shown that traffic shaping is able to decrease frame loss by a factor of up to 75 and MSE by a factor of up to 65, compared to direct VBR video broadcasting, which results in a substantial visible video quality improvement for broadcast applications. This study clearly shows that a traffic shaper customized to video applications is able to significantly enhance the quality of broadcast video streams transmitted over ATM networks, while shaping the stream to conform to negotiated QoS parameters. References [1] C. V. N. Albuquerque, M. Faerman, and O. Duarte, Implementations of Traffic Control Mechanisms for High-Speed Networks, IEEE Proceeding of Telecommunications Symposium, 1: , August [2] A. F. Atlasis, G. I. Stassinopoulos, and A. V. Vasilakos, Leaky Bucket Mechanism with Learning Algorithm for ATM Traffic Ppolicing, IEEE Proceeding of Computers and Communications, pp , July [3] T. R. Banniza, G. Eilenberger, B. Pauwels, and Y. Therasse, Design and Technology Aspects of VLSI's for ATM Switches, IEEE Journal on Selected Areas in Communications, 9(8): , October [4] P. Barri and J. A. O. Goubert, Implementation of a 16 by 16 Switching Element for ATM Exchange, IEEE Journal on Selected Areas in Communications, 9(5): , June [5] A. Catsoulis, Y. Kamaras, K. Kavidopoulos, and N. Mitrou, An Adaptive Shaper with Effective-Rate Enforcement for ATM Traffic, Third IEEE Symposium on Computers and Communications, pp , July [6] CCITT, Recommendation I.150: B-ISDN ATM Functional Characteristics, CCITT, Geneva, [7] W.-T. Chen, W.-S. Huang, and C.-H. Lin, A Policing Algorithm for MPEG Streams on ATM Network, IEEE International Conference on Communications, 1: , June [8] P. Cuenca, L. Orozoco-Barbosa, F. Quiles, and A. Garrido, Loss-Resilient ATM Protocol Architecture for MPEG-2 Video Communications, IEEE Selected Areas in Communications, 18(6): , June [9] I. Dalgic and F. A. Tobagi, Performance Evaluation of ATM Networks Carrying Constant and Variable Bit - Rate Video Traffic, IEEE Journal on Selected Areas in Communications, 15(6): , August [10] N. G. Duffield, K. K. Ramakrishnan, and A. R. Reibman, Issues of Quality and Multiplexing when Smoothing Rate Adaptive Video, IEEE Transactions on Multimedia, 1(4): , December [11] Generic Coding of Moving Pictures and Associated Audio Information: Video, ISO/IEC JTC1/SC29/WG11, MPEG-2 Draft International Standard, [12] J. Gu, M. Jurczyk, and C. W. Chen, Impact of ATM Traffic Control on MPEG-2 Video Quality, IEEE International Symposium on Circuits and Systems, pp , May [13] M. G. Hluchyj and M. J. Karol, Queueing in High- Performance Packet Switching, IEEE Journal on Selected Areas in Communications, 6(9): , December [14] J. S. M. Ho, H. Uzunalioglu, and I. F. Akyildiz, Cooperating Leaky Bucket for Average Rate Enforcement of VBR Video Traffic in ATM Networks, IEEE INFOCOM, pp , April [15] [16] J. Ivanova and M. Jurczyk, Computer Networks in The Encyclopedia of Physical Science and Technology - Third Edition, R. A. Meyers, ed., Academic Press, San Diego, California, [17] V. G. Kulkarni and N. Gautam, Leaky Buckets: Sizing and Admission Control, IEEE International Conference on Decision and Control, 1: , December [18] M. Li and Z. Tsai, Design and Analysis of the GCRA Traffic Shaper for VBR Services in ATM Networks, IEEE International Conference on Communications, 1: , August [19] M. Li and Z. Tsai, An ATM Traffic Shaper for Delay- Sensitive Delay-Insensitive VBR Services, IEEE International Conference on Information Networking, pp , January [20] W. Lou and M. E. Zarki, Quality Control for VBR Video over ATM Networks, IEEE Journal on Selected Areas in Communications, 15(6): , August [21] Mpeg Software Simulation Group, MPEG-2 Video Codec, Version 1.2, webpage: org/mpeg/ MSSG/#source, July [22] NIST ATM Network Simulator, Version 2.0, webpage: [23] M. Orzessek and P. Sommer, ATM & MPEG-2: Integrating Digital Video into Broadband Networks, Prentice Hall, Upper Saddle River, NJ, [24] P. Pancha and M. El Zarki, Leaky Bucket Access Control for VBR MPEG Video, IEEE INFOCOM'95, pp , April [25] S. V. Raghavan and S. K. Tripathi, Networked Multimedia Systems, Prentice-Hall, Upper Saddle River, NJ, [26] E. Rathgeb, Policing of Realistic VBR Video Traffic in ATM Network, International Journal of Digital and Analog Communication System, 6(5): , May [27] J. Rexford, F. Bonomi, A. Greenberg, and A. Wong, Scalable Architecture for Integrated Traffic Shaping and Link Scheduling in High-Speed ATM Switches, IEEE Journal on Selected Areas in Communications, 15(5): , June [28] Y. Wang and M. Jurczyk, Impact of Traffic Shaping in ATM Networks on Video Quality, ICPP Workshop on Parallel and Distributed Multimedia Systems, pp , August 2000.

12 12 IJCA, Vol. 10, N0. 3 Sept Yongdong Wang obtained his M.S. in Computer Science from the University of Missouri-Columbia, in In 1997, he graduated from Tsinghua University, China, with a M.S. in Computer Engineering. He is currently a principal Member of Technical Staff at Celox networks, INC., headquartered in Boston, MA. His research interests include high performance edge net-working with QOS, MPEG-2 over ATM network, ATM Internetworking, and Parallel/Distributed Systems. Michael Jurczyk obtained his Ph.D. in Electrical Engineering form the University of Stuttgart, Germany, in In 1996, he was a visiting assistant professor at the School of Electrical and Computer Engineering at Purdue University. He is currently an assistant professor at the Computer Engineering and Computer Science Department at the University of Missouri-Columbia. His research interests include parallel and distributed systems, interconnection networks for parallel and communication systems, ATM networking, and networked multimedia.

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