To reduce the cost of providing video on demand (VOD), one must lower the cost of operating each channel. This can be achieved by allowing many users

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1 Leverage Client Bandwidth to Improve Service Latency in a Periodic Broadcast Environment Λ Kien A. Hua 1 Ying Cai 1 Simon Sheu 2 1 School of Computer Science University of Central Florida Orlando, FL 32816, U.S.A fkienhua, caig@cs.ucf.edu 2 Department of Computer Science National Tsing Hua University Hsinchu, Taiwan 300, R.O.C sheu@cs.nthu.edu.tw December 4, 2001 Abstract Periodic broadcast has been shown to be very effective in addressing the bandwidth limitation in multimedia servers. Since this approach allows users to share a server stream, its bandwidth requirement is independent of the number of users the system is designed to support. Existing broadcast methods restrict clients to downloading data from no more than two channels at any one time. This constraint isremoved in this paper in order to leverage emerging "last mile" technologies such as ADSL and cable modems. We introduce a Client-Centric Approach (CCA) to exploit the vast client bandwidth. A client is allowed to use all its available channels to download data simultaneously. We carefully design the broadcast schedule so that this strategy does not flood the client with preloaded data. We prove the correctness of this approach, and provide analytical evaluations to show that it performs significantly better than Skyscraper Broadcasting technique that has been shown to offer very good performance. KEYWORDS: Multimedia communication, multicast, server bandwidth, service latency. 1 Introduction The unit of server capacity required to support the continuous delivery of a video stream is referred to as a channel. The number of channels available to a server is determined by the smaller of its I/O and communication bandwidths. The simplest video delivery technique uses a dedicated channel for each video session. This scheme is too expensive for most applications. An example of this approach is the Time Warner's Full Service Network project in Orlando. They had to use eight SGI Challenge servers in order to achieve a system bandwidth of 1,000 MPEG-1 streams. Although 4,000 homes participated in the program, their experience indicated that at most 1,000 use the service simultaneously. Based on this statistics, one would need about 31,000 SGI Challenge servers in order to provide the service to the greater Orlando area. Obviously, this is prohibitively expensive for a production system. Λ This research is partially supported by the National Science Foundation grant ANI

2 To reduce the cost of providing video on demand (VOD), one must lower the cost of operating each channel. This can be achieved by allowing many users to share channels using multicast. Twomulticast approaches have been considered: ffl Non-Periodic Multicast: Requests by multiple clients for the same video arriving within a short time duration can be served together as a group using a single stream. This is referred to as batching in [6]. When a server channel becomes available, the scheduler selects a batch to multicast according to some scheduling policy. For instance, the Maximum Queue Length (MQL) proposed by Dan et al. [6], selects the batch with the most number of pending requests to serve first. The objective of this strategy is to maximize the server throughput. Other non-periodic multicast schemes are presented by Dan et al. in [6, 7], by Aggarwal et al. in [1], and by Hua et al. in [5, 9]. In particular, the Patching scheme studied in [9, 4, 8, 15, 3] does not require batching. Instead, a client can join a recent multicast. To provide the missing data, the server initiates a patching channel to transmit only the leading portion of the video. ffl Periodic Broadcast: The videos are broadcast periodically, i.e., a new stream is started every t minutes for each video. As a result, the worst service latency experienced by any client is guaranteed to be less than t minutes. The advantage of this approach is that the bandwidth requirement is independent of the number of subscribers the system is designed to support. This approach, therefore, has the ability to serve avery large user community using minimal hardware. Some recent periodic broadcast techniques are presented by Dan et al. in [6], by Viswanathan et al. in [16], by Aggarwal et al. in [2], by Hua et al. in [12], by Paris et al. in [13, 14]. In these techniques, each video file is partitioned into K fragments of increasing sizes, each repeatedly broadcast on a dedicated channel at the playback rate. To achieve low service latency, the size of the first fragments is made very small to allow them to be broadcast more frequently. It was proved in [12] that if a client can download data from two channels simultaneously, then the continuous playback of the video can be guaranteed. Since various videos are not accessed with the same frequency, a good scheduler should employ different multicast techniques for different videos. In general, non-periodic multicast is better for less popular videos. For highly demanded videos, periodic broadcast is the better choice. It was shown in [6,7]thatahybrid of these two approaches offers the best performance. In these techniques, some of the server channels are reserved for periodic broadcast of the most popular videos. The remaining channels are used to serve the rest of the videos using non-periodic multicast. An adaptive hybrid approach was considered in [10, 11]. In this scheme, the optimal number of periodic broadcast channels is determined dynamically. This approach, therefore, can adapt to a changing workload. In general, most of the service requests (e.g., 80%) are for a few (e.g., 10% to 20%) very popular videos [6, 7]. The effectiveness of the periodic broadcast scheme, therefore, is very crucial to the overall performance of a video server. In this paper, we focus on the dissemination of these highly popular videos. We investigate techniques to leverage emerging "last-mile" technologies, such as asymmetric digital subscriber line (ASDL) and cable modems, to further reduce the service delay. The challenge is to exploit the vast client bandwidth without flooding it with pre-loaded data. Existing periodic broadcast schemes assumed that client bandwidth was very limited, i.e., each client could download data from no more than two channels simultaneously. This condition has been improved dramatically as a result of recent advancement in network communication technology. For examples, ffl ADSL (Asymmetric Digital Subscriber Line): This scheme connects an ADSL modem to each

3 end of a twisted-pair telephone line to create three information channels: a high speed downstream channel, a medium speed duplex channel, and a plain old telephone service channel. This technology achieves speeds currently of 8 Mbps in one direction, and eventually speeds as high as 50 Mbps. ffl FTTC (Fiber To The Curb): In this scheme, the telephone company runs optical fiber from the end office into each residential neighborhood, terminating in a junction box." The copper local loops are now so short that it is possible to run duplex data at a very high bandwidth. ffl FTTH (Fiber To The Home): This solution runs fiber into everyone's house. Every one can have an OC-3 or even higher carrier. ffl HFC (Hybrid Fiber Coax): All of the above telephone company technologies are point-to-point. HFC is a completely different approach preferred by cable TV providers. In this scheme, the current 300- to 450-MHz coax cables are replaced by 750-MHz coax cable to achieve a total of 2 Gbps of new bandwidth. To ensure that each house is allocated enough bandwidth, the head-ends can be moved deeper into the neighborhoods so that each cable runs past fewer number of houses. Except for FTTH that will likely remain expensive for years, ADSL, FTTC and HFC are widely available today. Such large media companies as Tele-Communications and Time Warner Communications are heavily touting their cable modem services. was installed in 10 million homes by the end of July 1998, while Time Warner's Road Runner has 420,000 subscribers in New York city alone in For ADSL, service providers are scrambling to get ADSL equipment in place. US West Communications is offering its ADSL MegaBit Services in 15 U.S. cities. Bell Atlantic has ADSL lines installed in four U.S. states. A group of PC companies including Compaq, Intel, and Microsoft, along with several major telecom providers calling themselves the Universal ADSL Working Group, has announced plans to push ADSL as an affordable option for home PC users. In this paper, we introduce a generalized periodic broadcast technique called Client Centric Approach (CCA). Unlike existing methods that limit the number of download channels to two at any one time, CCA allows a client to download data using all its available channels simultaneously. In other words, we exploit client bandwidth to further reduce the service latency. This is achieved without requiring the client tohave more buffer space. To the best of our knowledge, this approach has not been studied in the literature. The remainder of this paper is organized as follows. We discuss some existing periodic broadcast techniques in Section 2. The proposed technique is presented in Section 3. Its correctness is proved, and its performance analyzed in Section 4. In Section 5, we compare its performance to that of a recent periodic broadcast technique. Finally, we give our concluding remarks in Section 6. 2 Related Work An earlier periodic broadcast scheme was proposed in [6] by Dan, Sitaram, andshahabuddin. Since this approach broadcasts each video every fixed interval, the service latency can be improved only linearly with the increases in the server bandwidth. It was later observed by Viswanathan and Imielinski [16] that this latency can be reduced substantially by prefetching the video data into the disk buffer at the receiving end. This observation has inspired a series of new periodic broadcast techniques we will discuss in this section. Without loss of generality, we assume in this paper that the system has only one video. In general, a system with n video files, can be treated as n virtual servers, each handles a

4 distinct video file. The system bandwidth is divided among these virtual servers in proportion to the length of the video they serve. Any periodic broadcast technique discussed in this paper can then be applied independently to each of these virtual servers. A periodic broadcast design consists of four components: a channel design, a data fragmentation technique, a broadcast schedule, and a playback strategy. A typical design has the following characteristic: ffl Channel design: The bandwidth of the server is divided equally into K logical channels, each has a bandwidth of B channel (= Bserver K, where B server is the bandwidth of the server). ffl Data fragmentation technique: The video file is divided into K fragments of increasing sizes. ffl Broadcast schedule: The ith video fragment is repeatedly broadcast on the ith channel. ffl Playback strategy: Each client has a number of loader processes and a playback process. Each loader process tunes into its preassigned channels at different times to download the corresponding video fragments. As the loader processes fill the disk buffer, the playback process consumes the data in the buffer and renders them onto the screen. For such a design to work, the i + 1st fragment must become accessible before the consumption of the ith fragment is finished. This is referred to as the continuity principle in [16]. To ensure that the worst access latency is small, it is desirable to make the first fragment as small as possible provided the continuity principle is not violated. Several techniques have been proposed to achieve this goal [16, 2, 12]. They are discussed in the following. In [16], the video file is partitioned into K segments of geometrically increasing size. That is, if D is the playback duration of the video and D i denotes the playback duration of segment i, then we have D i+1 = ff D i, and D = D 1 Λ (1 + ff + ff 2 + :::+ ff K 1 ), where the optimal value for ff is the Euler's constant (i.e., e = 2:72). This technique is called Pyramid Broadcasting (PB) due to the fact that stacking up the video segments of increasing sizes resembles the shape of a pyramid. We will refer to the geometric series, [1;ff;ff 2 ;ff 3 ; ;ff K 1 ], as a broadcast series in this paper. Although PB can radically improve the performance of the standard technique which uses a uniform broadcast series, the bandwidth of each channel must be made significantly larger than the playback rate in order to satisfy the continuity principle. A consequence of this requirement is that a larger buffer space must be available at the receiving end in order to buffer the preloaded data. Usually, abuffer space larger than 80% the size of the video file is needed [12]. To address this drawback, Aggarwal, Wolf, and Yu proposed a technique called Permutation Based Broadcasting (PBB) [2]. PPB is similar to PB except that each channel is further divided into, say s, subchannels, and a replica of the corresponding segment is repeatedly broadcast on each of these subchannels with a uniform phase delay. Effectively, this approach reduces the incoming data rate by a factor of s. This approach is able to reduce the space requirement to around 50% of the video file. To further reduce the data rate, PPB permits a download process to pause a download stream to allow the playback process to catch up. This download process will "wake up" at a later time, and tune into the broadcast of the same video segment on another subchannel to collect the remaining data. Such synchronization mechanism, however, is difficult to implement since the tuning must be done at the right moment during a broadcast. To avoid the complexity discussed above, another technique, proposed by Hua and Sheu in [12], only tunes to the beginning of any broadcasts as in the original PB. They were able to achieve more

5 savings than PPB using a much simpler synchronization mechanism. The following broadcast series was used in their scheme: [1; 2; 2; 5; 5; 12; 12; 25; 25; 52; 52; ] which is generated using the following recursive function: f(n) = 8>< >: 1 if n =1; 2 if n = 2 or 3; 2 f(n 1) + 1 if n mod 4=0; f(n 1) if n mod 4=1; 2 f(n 1) + 2 if n mod 4=2; f(n 1) if n mod 4=3: The first number in the above series signifies that the size of the first fragment is one unit, say D 1 ; the size of the second one is two units, or 2 D 1 ; similarly, thesize of the third segment is two units; the fourth one is five; and so forth. We note that D i denotes the playback duration of the segment. Its spatial size can be computed by multiplying D i by the playback rate. This broadcast series allows each client to use two channels with much smaller bandwidth to download data. Another advantage of this approach is that it allows one to constrain the sizes of the larger video segments. That is, an upper limit, W, is imposed on the broadcast series to restrict the segments from becoming too large. If some segment is larger than W times the size of the first segment, SB forces its size to be W D 1. Since the storage requirement is determined by the size of the largest segment. This approach effectively reduces the demand on the buffer space. This technique is called Skyscraper Broadcasting (SB) because stacking up the data fragments in the order they appear in the video file resembles a very tall skyscraper (instead of a much shorter and very wide pyramid as in the case of PB and PPB.) W is called the width of the skyscraper," which is related to the service latency as follows: D Service Latency = D 1 = P Ki=1 min(f(i);w) ; where K is the number of video segments. The broadcast strategy used in SB is illustrated in Figure 1. It shows that each video segment is repeatedly broadcast on its dedicated channel. The reception of segments at the receiving ends is done in terms of transmission group, which is defined as consecutive segments having the same size. For example, according to the broadcast series [1; 2; 2; 5; 5; 12; 12; 25; 25; 52; 52;:::], the first segment forms the first group; the second and third segments form the second group (i:e:, 2; 2"); the fourth and fifth form the third group (i:e:, 5; 5"); and so forth. A transmission group (A; A; ;A) is called an odd group if A is an odd number; otherwise, it is called an even group. We note that the odd groups and the even groups interleave in the broadcast series. To receive and playback these data fragments, a client uses three threads of control, an Odd Loader, an Even Loader, and a Video Player. The Odd Loader and the Even Loader download data at the playback rate. They are responsible for tuning to the appropriate logical channels at different times to download the odd groups and the even groups, respectively. Each loader downloads its groups one at a time in its entirety; and the segment(s) within a group is (are) downloaded sequentially. The groups are downloaded in the order they occur in the video file. These three service routines share a local buffer. As the Odd Loader and Even Loader fill the buffer with the incoming data, the Video Player consumes the data at the rate of one broadcast channel. To support this pipelining strategy, it was shown in [12] that each receiving end needs a buffer space computed as follows: Buffer Size = D 1 (W 1). The performance results reported in [12] indicates that SB outperforms both PB and PPB in terms of space requirement and service latency. PB, PPB and SB have made significant contribution in improving the periodic broadcast approach.

6 S1 S2 S3 S4 S5 S6 (a) Segmentation for SB channel 1 channel 2 channel 3 channel 4 channel 5 channel 6 (b) Broadcasting Scheme for SB Figure 1: Skyscraper Broadcasting (6 channels) PB was the first to use a nonuniform broadcast series. This work has inspired the work done for PPB and SB which successfully addressed some of the drawbacks in PB. Only SB was discussed in details here since it has been shown to offer better performance, and we want to compare its performance to that of our technique. The interested reader is referred to [16] and [2] for more detail on PB and PPB, respectively. Although PB, PPB and SB offer outstanding performance, there is still room for improvement. Let us consider a generic broadcast scheme which uses the following broadcast series: [e 1 ;e 2 ;e 3 ;e 4 ; ;e K 1 ;e K ] The worst waiting time for the service is the worst access time of the first data fragment which is broadcast on Channel 1. Therefore, the worst service latency can be computed as follows, where b is the playback rate: Service Latency = b D 1 B server K = b B channel D P Ki=1 (1) e i Obviously, the service latency is not good enough if K is too small. This is the case for PB and PPB. To ensure the continuity ofthe playback stream, PB requires that ff = B channel b. We can compute the value of K as follows: K = B server = B server B channel b ff The performance of PB is optimal when ff has the value of the Euler's constant, or However, this corresponds to a K value which isvery small compared to that used in SB. This is the key to the performance gain provided by SB. It increases the value of K by letting B channel = b which is 2.72 times slower than the channel bandwidth used in PB. With this strategy, SB is able to use a K value which is 2.72 times larger than those used in PB (see Equation (2)). In spite of its significantly better performance, SB has one drawback. The very slow growth rate of its broadcast series limits the value of the sum term P K i=1 e i " which appears in the denominator of Equation (1). This in turn delimits the saving achievable for the service latency. In this paper, we introduce a novel periodic broadcast scheme. Our technique is able to use a broadcast series with a very fast growth rate; yet we make minimal compromise on the K value. This is achieved by taking advantage of the bandwidth available at the receiving end. Although PB (2)

7 also downloads data at a very high data rate, the data are arriving in bursts and the communication resources at the receiving end are wasted most of the time. Instead of using only two very-highbandwidth channels to handle the bursts of data as in PB, our idea is to organize the client bandwidth into many smaller channels, each has the bandwidth of the playback rate, to steadily download the different data segments of the same video simultaneously. The number of channels available to a client depends on the capability of its network interface card (NIC). For instance, if an OC-3 adapter is used to receive MPEG-2 data, the client can have up to about 35 download channels. We will demonstrate later that trading more download bandwidth for better service latency does not have to incur higher storage costs. In fact, CCA outperforms SB by a significant margin under the same buffer condition. 3 Proposed Technique: Client-Centric Approach We refer to the proposed technique as a Client-Centric Approach (CCA) since the design decisions are driven primarily by the capability of the receiving end, i.e., buffer space and download bandwidth. We describe the details of this scheme in the following. 3.1 Channel Design As in SB, CCA divides the communication bandwidth of the server into K logical channels, each has the bandwidth of the playback rate. Each logical channel is used to repeatedly broadcast a distinct video segment. We discuss the data fragmentation technique in the following subsection. 3.2 Data Fragmentation CCA uses the following broadcast series: Group 1 : ::: 2 c 1 Group 2 : 2 c 1 2 1Λ(c 1)+1 ::: 2 2Λ(c 1) Group 3 : 2 2 (c 1) 2 2 (c 1)+1 ::: 2 3 (c 1).... Group i : 2 (i 1) (c 1) 2 i (c 1)+1 ::: 2 i (c 1).. Group g : 2 (g 1) (c 1) 2 g (c 1)+1 ::: 2 K d K c e The above series is generated using the following recursive function: f(n) = 8>< >: 1; if n =1; 2 f(n 1) if nmod(c +1)6= 0; f(n 1) if nmod(c +1)=0:. This generating function can also be presented in a close form as: f(n) =2 n d n c e, where 1» n» K. We note that the term c in the generating function denotes the maximum number of channels each client can tune into at any one time to download several data segments simultaneously. As in SB, we can also limit the sizes of the data fragments in CCA in order to conserve disk space. In this case, the sizes of the larger fragments are fixed at W D 1, i.e., W times the playback duration.

8 of the first data fragment. We will refer to these data fragments as W-segments in this paper. 3.3 Transmitting and Receiving of Video Segments The transmission of data fragments at the server end is straightforward. The server multiplexes among the K logical channels; each is used to repeatedly broadcast one of the K video segments at the playback rate. This strategy is illustrated in Figure 2 for K =6andc =3. At the client end, reception is done in terms of transmission groups, which is explained as follows. As shown in the broadcast series given in Section 3.2, the video segments are grouped into g transmission groups, where g = d K c e. Each group has c items except that the last group has K (d K c e 1) c items. The grouping strategy has the following characteristics. The size of the last segment ofagroupmust be equal to the size of the first segment of the next group. It will be clear later that this property iscontrived to ensure the continuity of the playback stream. S1 S2 S3 S4 S5 S6 (a) Segmentation for CCA channel 1 channel 2 channel 3 channel 4 channel 5 channel 6 (b) Broadcasting Scheme for CCA Figure 2: Client-Centric Approach (K=6, c=3) To receive and playback the data fragments, a client uses c + 1 service routines: c data loaders, L 1 ;L 2 ; ;L c, and one video player. The client must multiplex itself among these routines, and therefore must be multi-threaded. Each data loader can download data at the playback rate. The data segments are downloaded in g rounds. During each round, say r, the rth transmission group is downloaded as follows. Each of the c loaders is responsible for downloading its respective data fragment at its next broadcast. When the download of the current group has been completed, the loaders proceed to download the next transmission group, i.e., r + 1st group, in the same manner. If W is used to constrain the size of the larger segments, the client switches mode to download the W -segments sequentially using only one loader. This strategy helps to conserve the client buffer space. We illustrate the mechanism for receiving data and playing back video in Figure 3. We note that the player can begin the playback as soon as the first segment of the video becomes accessible. Figure 3 shows a snapshot of some client during the downloading of some transmission group. It shows that the first loader L 1 is free. The player is consuming the data downloaded by L 2. The last loader L g is still waiting for its data segment to occur on the server channel i + c 1. 4 Analyses In this section, we discuss the correctness of the proposed technique, and analyze its performance and hardware requirements.

9 Server : : : channel 1 channel i channel i+1 channel i+2 channel i+c-1 channel K L1 L2 L3... Lg... Disk Buffer Player Client Figure 3: Downloading data and playing back 4.1 Correctness The proof of continuity is done in two steps: 1. We show that the segments in the same group can be downloaded continuously. 2. We prove that the first segment of the next transmission group is accessible at the end of downloading the current transmission group. ffl Continuity of the segments in the same group Since CCA uses a dedicated download channel for each segment ina group, to prove the continuity within a transmission group we need only show that one of the following conditions is satisfied for any two consecutive segments S i and S i+1 : 1. S i+1 becomes accessible before the consumption of S i is finished; or 2. there is a broadcast of S i+1 which immediately follows any broadcast of S i. One of these criteria is easily met since the corresponding sizes D i and D i+1 of anytwo consecutive segments S i and S i+1 of a group must satisfy one of the following conditions: D i+1 =2Λ D i or D i = D i+1 = W D 1. As a result, there are only two possible alignments for broadcasting these two segments: they are either started at the same time, or finished at the same time (Figure 2). In the former case, two loaders can be used to begin downloading the two segments at the same time. In the latter case, the download of S i+1 immediately follows the download of S i. In either case, the continuity of the playback stream is ensured. Hence, the continuity within a transmission group is guaranteed. ffl Continuity across a group boundary Let S l and S f be the last segment and the first segment of any two consecutive groups l and f, respectively. To prove the continuity across the group boundary, we need to show that S f must become accessible before the consumption of S l is finished. This can be guaranteed if the following two conditions are true: 1. The first loader L 1 must be available before the consumption of S l is finished.

10 2. There must be a start of a broadcast of S f immediately following any broadcasts of S l. The first condition is easily satisfied because L 1 is used to download the smallest segment of group l. L 1 mustbecomefreeby the end of downloading group l even if all c loaders were able to start at the same time to download their respective data segments simultaneously. The second condition must also be true since the sizes of S l and S f are the same, and their broadcasts always start and end at the same time (Figure 2). In this case, the client canusel c and L 1 to download S l and S f, respectively. 4.2 Access Latency We first assume that W =2 K d K c e. That is, we do not constrain the sizes of the data segments. Since each data segment is broadcast at the playback rate, we can compute the worst access latency as the length (i.e., playback duration) of the first video segment. That is, Access Latency = D 1. According to the CCA's broadcast series, the video is fragmented into K segments which form g groups. Each group has c segments, except the last one which has only K c Λ (g 1) segments. The sizes of the segments in each group increase exponentially. Since the last segment in group i has the same size as the first segment in group i + 1, the broadcast series becomes an exponential series if we takeaway the first segment ineach group except the first group. Using this property, we can compute the length of the video as follows: D = D 1 2 X 64 K d K c e i=0 Thus, the access latency is, D 1 = X d K 2 i c e 1 + D i=1 2 i (c 1) K d K c e c 1 2(c 1) d K c e 1 2 (c 1) 1 We note that if the client's bandwidth limits c to one, the broadcast series has K groups, each has only one segment. In this case, CCA experiences the worst latency since it degenerates into a uniform broadcast with the following broadcast series: [1; 1; 1; ; 1; 1]. On the other hand, if c = K, the broadcast series becomes [1; 2; 2 2 ; 2 3 ; ; 2 K 1 ] which forms only one group. As a result, each client can use K loaders to download all K segments of the video simultaneously. Under this circumstance, we witness the best performance. In general, we have a spectrum of performance levels corresponding to various values of c between 1 and K. Let us now discuss the case of constraining the size of the larger video segments to W D 1. Under this condition, the access latency can be computed as follows: D 1 = P p 1 i=1 D f(i)+w (K p +1); where f is the generating function for the broadcast series, and p is the index (or position) of the first W -segment in the series.

11 4.3 Buffer Space Requirement Again, we first assume that W =2 K d K c e. That is, we do not constrain the sizes of the data segments. Under this condition, since the download bandwidth is larger than the playback rate, associating with each value of c is a corresponding requirement on the buffer size at the receiving end. We investigate the storage requirement in the following. We note that the worst space requirement occurs if all c loaders can start to download the c segments simultaneously for each group. Under this condition, the time required to download each group is the time it takes to download the last segment in the group. The amount of data consumed during this same time period must also be equal to the playback duration of the last segment in the group. The amount of data consumed by the video player by the end of the download process, therefore, can be computed as the sum of the playback duration of the last segment ofeach group as follows: Consumed Data = D 1 2 X 3 2 i (c 1) +2 K d K c 5 e 4 g 1 i=1 The storage requirement can be computed as the amount of data (in terms of playback duration) accumulated in the buffer at the end of the download process as follows: Buffer Size = D Consumed Data = D 1 = D 1 = D 1 X i=0 K g 1 2 i = D K D 1 2 K g 1 2 K d K c e 1 (3) Let us now consider the case when W < 2 K d K c e. Let p be the index of the first W -segment inthe series. If we regard the subseries [1; 2 1 ; ; 2 p 1 ] as a series, then its storage requirement isd p 1 D 1 according to Equation (3). Since the client receives the W -segments sequentially at the playback rate, the buffer size does not grow any further after the download process reaches the pth data segment. Thus, the storage requirement for CCA considering the W factor is: Buffer Size = D p D 1 =(W 1) D 1 : (4) Comparing Equations (3) and (4), we notice that substantial saving in storage cost can be achieved using the W factor. This fact is very critical to the CCA approach. It says that although using more download bandwidth to improve service latency can potentially incur a higher storage cost, the W factor can be used to control this cost. We will give analytical results in the next section to show that CCA outperforms SB by a wide margin under the same storage condition. 5 Performance Study We analyze the performance of the proposed technique in this section by comparing its performance to that of SB which has been shown to provide the best performance today [12]. Since we are primarily interested in the relative performance of the two techniques, we assume in our study that the system

12 has only one video. As we have discussed previously, if the system has n videos, the server bandwidth can be thought as divided evenly among n virtual servers. Each server is used to serve one of the n videos. Thus, the results reported in this section are also valid for systems with many videos. The performance parameters are given in Table 1. We note that the video is assumed to be encoded using MPEG-1 with the average playback rate of 1.5 Mbits/sec. We are interested in the server bandwidth ranging from 6.0 Mbits/sec to 18.0 Mbit/sec. For bandwidth less than 6.0 Mbits/sec, CCA can use at most three download channels and its performance is not significant better than SB. On the high end, we stop at 18.0 Mbit/sec since this is large enough to show the trends of the various design schemes. As for the client bandwidth, we choose the range from 3 Mbits/sec to 12 Mbits/sec because 3 Mbit/sec is required by SB, and 12 Mbits/sec is more than adequate for CCA to show its outstanding performance. It is not very interesting to make the access latency any smaller. Parameter Range Number of videos 1 Video length 120 minutes Playback rate 1.5 Mbit/sec (MPEG-1) Server Bandwidth (Mbits/sec) Client Bandwidth (Mbits/sec) Table 1: Parameters used for the performance evaluation. We choose worst service latency as our performance metric. We perform sensitivity analyses to investigate the effect of client bandwidth, server bandwidth, and buffer size on this metric. The formulae needed for this study have been derived in Section 4. For the convenience of the reader, we repeat them in the Table 2: We note that although the equations look essentially identical for both Metric Skyscraper Client-Centric Broadcasting (SB) Approach (CCA) Buffer Size D 1 (W 1) D 1 (W 1) D Service Latency P K P D i=1 min(f (i);w ) p 1 i=1 f (i)+w (K p+1) Table 2: Formulae for computing buffer requirement and service latency. techniques, their optimal values for W are different because they use very different broadcast series. As a result, their storage requirements are different for a given performance target. We present the performance results in the following subsections. In Sections 5.1 and 5.2, we assume that the sizes of the data segments are not constrained by the W factor. This issue is investigated in Section 5.3 when we investigate the effect of buffer space on the service latency. 5.1 Effect of Client Bandwidth on Service Latency In this subsection, we analyze the effect of client bandwidth on the service latency of the two broadcast techniques. We vary the bandwidth of the client from 3.0 Mbits/sec to 12.0 Mbits/sec while the bandwidth of the server is fixed at 9.0 Mbits/sec and 12.0 Mbits/sec, respectively, for two different cases. The access-latency curves for SB and CCA under these conditions are plotted in Figure 4. It

13 shows that the performance of SB is not affected by the changes in the client bandwidth. This is due to the fact that SB fixes the two download channels at 1.5 Mbits/sec (i.e., the playback rate), and is unable to exploit the extra resources available to the client. In contrast, the service latency drops rapidly under CCA. It shows that the new technique is very effective in taking advantage of the client bandwidth to reduce service latency. For instance, when the bandwidth of the server is 9.0 Mbits/sec and the bandwidth of the client is 7.5 Mbit/sec, CCA provides a performance almost one time better than that of SB. We notice that the curves for CCA eventually become flat. This is due to the limitation of the server bandwidth as evidenced by the fact that the performance of CCA is improved when the server bandwidth is increased from 9.0 Mbits/sec to 12.0 Mbits/sec. We note that server bandwidth is very scarce since it is shared by all clients. Client bandwidth, however, is plenty for many VOD environment. As an example, a client station equiped with an OC-3 NIC is capable of 100 MPEG-1 channels. Most of these bandwidth would be wasted if a broadcast scheme does not exploit it. 6 5 Access Latency (Minutes) SB server bandwidth = 9Mbits/s CCA server bandwidth = 9Mbits/s CCA server bandwidth = 12Mbits/s Client Download Bandwidth (Mbits/s) Figure 4: Access latency under various client bandwidths. 5.2 Effect of Server Bandwidth on Service Latency In this study, we investigate the effect of the server bandwidth on the two broadcast schemes. The results of our study are plotted in Figure 5 for various client bandwidth (i.e, 2» c» 4). We note that although SB performs better than CCA if only two download channels are used, CCA is capable of using the remaining client bandwidth to improve its latency to well below the level provided by SB. For instance, when the bandwidth of the server is 10.5 MBits/sec, CCA using four channels enjoys a service latency which is almost twice as good as that offered by SB. We also notice that the performance of both schemes are sensitive to the bandwidth of the server. This is due to the fact that a smaller server bandwidth entails a larger broadcast period for each broadcast channel. Therefore, it takes longer to wait for the next occurence of the first segment. This observation is consistent with the plot shown in Figure Effect of Buffer Size on Service Latency In the previous analyses, we assumed that CCA had sufficient buffer space to allow the client to exploit its communication capability. In this subsection we compare the performance of SB and CCA under the same buffer condition. We assume that both schemes use their best value of W to conserve disk space. The results of this study are plotted in Figure 6. The size of the client buffer is varied between

14 12 10 SB CCA when c=2 CCA when c=3 CCA when c=4 Access Latency (Minutes) Server Broadcast Bandwidth (Mbits/s) Figure 5: Access latency under various server bandwidths CCA when c=2 CCA when c=3 CCA when c=4 SB Access Latency (min) Storage Buffer Size (MBytes) Figure 6: Access latency under various buffer size. 150 MBytes and 550 MBytes. It shows that using more than two download channels allows CCA to outperform SB by a very significant margin. These savings can be achieved without having to pay for additional storage costs. For instance, when the buffer size is 350 MBytes, CCA using 4 channels outperforms SB by a factor of two. We note that requiring two additional channels does not usually translate into a higher system cost. The extra channels usually come standard with many of today's communication adapters intended for video-on-demand applications such as OC-3. 6 Concluding Remarks In this paper, we surveyed several periodic broadcast schemes for video-on-demand applications. We discussed their drawbacks and proposed a new solution called Client-Centric Approach (CCA). While existing techniques primarily focus on the server design, CCA also exploits the client capability. We showed that it is possible to leverage the bandwidth at the receiving end to improve the performance of the server. This approach allows multimedia servers to take advantage of emerging technology such as ADSL and cable modems. We formally proved the correctness of CCA by showing that the continuity of the playback streams is guaranteed. To substantiate its good performance, we provided analyses to compare its service latency with that of Skyscraper Broadcasting technique which has been shown to offer excellent performance.

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