Asynchronous and Reliable On-Demand Media Broadcast Hrvoje Jenkaˇc Institute for Communications Engineering (LNT) Munich University of Technology (TUM) Munich, Germany
[email protected] Thomas Stockhammer Nomor Research Bergen, Germany
[email protected] Wen Xu BenQ mobile Munich, Germany
[email protected] Abstract We discuss wireless broadcasting of multimedia streams within a framework which allows asynchronous media access. Receivers subscribe at any time to the ongoing broadcast session, but are still able to display the media stream from the beginning. A fully scalable broadcasting scheme is presented where the media stream is appropriately segmented and segments are protected by fountain codes. Erasure based decoding as well as soft-decoding is discussed. Asynchronous data reception and full reliability are achieved at the same time. Depending on its receiving conditions the receiver adapts its initial playout delay to guarantee high reliability of successful playout.
I. I NTRODUCTION Mobile TV and other wireless video broadcasting services to mobile users are recently experiencing a rejuvenation due to advanced terminal capabilities, new network infrastructures and significant improvements in media coding efficiency. Traditionally, in video broadcast services all receivers retrieve an identical media stream synchronously in a regular TV fashion. Late tune-in into movies, sports events, news broadcasting, or shows is commonly supported by random access points in the bit-stream. However, despite of the capability to almost instantaneously join an ongoing program, missing a significant amount of earlier content can usually not be avoided. In contrast, media-on-demand (MoD) schemes aim to provide the possibility of tune-in of receivers at generally arbitrary time without missing the start of the program. To accomplish this feature, trivial schemes offer the service over conventional unidirectional multimedia streaming services with individual connections between the transmitter and the receivers. However, with increasing amount of subscribers, the system may not scale appropriately and may require a significant amount of transmission bandwidth proportional to the number of subscribers. Figure 1 shows a wireless MoD scenario based on individual point-to-point (p-t-p) links: Each subscriber requires a transmission bit rate R = RM within some fixed network and a corresponding bandwidth B = b over some wireless broadcast network. However, note that though receivers request the media stream
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at arbitrary time, i.e., asynchronously and independent of each other, for a higher number of service subscribers it is likely that the streaming periods of several receivers, in our case K, overlap. In some cases it might result in a worst-case bandwidth requirement of Btot = K · b. p-t-p p-t-p
B=b B=b
k=1
p-t-p B=b R = K · RM
. . .
Streaming Server
k=2
k=K
Fig. 1. Trivial solution for a wireless Video-on-Demand system. For each receiver an individual p-t-p channel is established.
In general, bandwidth in a broadcast system is an extremely costly and valuable resource, especially in wireless environments. To eliminate the drawbacks of setting up multiple p-t-p connections for reliable broadcast systems, e.g., cable networks, a huge variety of MoD broadcasting schemes have been proposed and evaluated [1], [2]. All of them have in common that media can be requested asynchronously by receivers without the demand for individual p-t-p connections. Usually, these schemes require some media stream pre-processing. The required pre-processing is performed in such a way that an acceptable fixed bandwidth expansion is achieved which does not depend on the number of receivers. In addition, the pre-processing is designed such that the worst-case start-up delay is still in an acceptable range. Although these schemes perfectly solve the issue of asynchronous media access, they do not provide any loss resilience if transmission over some harsh wired or wireless broadcast networks is aimed. Many attractive packet-based delivery networks such as the Internet suffer from losses which usually cannot be recovered by repeat request mechanisms in a multicast/broadcast environment. Therefore, it has been proposed to extend advanced MoD services in packet-lossy environments [3], [4] by means of reliability mechanisms. Specifically, it is proposed to add additional Forward Error Correction (FEC) based on traditional channel codes or alternatively on so called fountain codes [5]. Though FEC results in additional bandwidth expansion, the benefits of such approaches will be made apparent in this work. Broadcasting MoD services to mobile users is definitely challenging due to hostile transmission conditions and severe bandwidth constraints. Figure 2 illustrates the addressed system design for asynchronous MoD services over wireless channels: Since disturbances due to fading, interference, and noise are experienced in wireless networks, the original media stream is not only pre-coded to allow Asynchronous Access (AA) but also concepts for FEC are introduced. Assume that we have some overall bit-rate, or equivalently some normalized bandwidth available. The pre-coding for AA and FEC is constructed in a such way that each subscriber with arbitrary receiving conditions is able to adapt to its individual channel characteristics, simply by adjusting the playout delay of the requested MoD session. However, in contrast to other reception-scalable frameworks, in our discussed scheme each receiver will still be able to receive the full quality video stream. Receiving conditions are traded with average or worst-case initial start-up delay of the MoD service. More specifically, we discuss the question, how long a receiver with specific receiving conditions should delay the start of the media presentation in order to guarantee certain presentation reliability. The rest of the paper is organized as follows. We outline the MoD framework, related problems and
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possible solutions in the next two sections. Then we will focus on asynchronous FEC based on fountain codes, followed by its application to asynchronous wireless streaming. The analytical findings are validated through performance evaluations. Finally, we give some concluding remarks. p-t-M B =ω·b
k=1
AA / FEC Pre-coding
R = ω · RM
. . .
k=2
k=K
R = RM Streaming Server
Fig. 2. Point-to-Multipoint (p-t-M) solution: Appropriate pre-coding allows asynchronous media access and reliable transmission over wireless broadcast channels.
II. P RELIMINARIES A. Multimedia Abstraction In unicast streaming environments, the receiver requests a certain multimedia clip. Assume that the media stream P is represented as an encoded packet-stream, containing a sequence of N independently accessible data units, i.e., P = {P1 , . . . , PN }. Each data unit has a certain size |Pn | and some timing information in form of a Decoding Time-Stamp (DTS) tDTS (n) indicating when this data unit must be decoded relative to tDTS (1). Without loss of generality, we assume tDTS (1) = 0. The streaming server starts transmitting the first data unit and with some delay the decoder starts receiving the first data unit at time tr . Sender and receiver continue to transmit and receive further data units. Let us assume that data units are received error-free and that a sufficiently large buffer is available to store the incoming data units. At some arbitrary time td,1 , the decoder starts decoding the first data unit. With this decision, the schedule for the latest decoding time for all remaining data units is determined as td,n = tDTS (n) + td,1 . If we neglect processing delays, the initial playout delay ∆ between the request of the user to view the stream and the playout of the first data unit, ∆, is determined as ∆ = td,1 − tr . Many channels have the property that the delivery time of data units varys due to jitter on the network. Then it is obvious that there exists a tradeoff in the selection of ∆: Large initial delays provide high probability that all data units are available at decoding time at the receiver, but they are annoying to the end user. In contrast, short initial playout delays reduce the buffering period which is required to compensate delay jitter introduced through the transmission network. If the data unit n is not available at the receiver at time td,n it is in general considered as lost, since it can not be presented in time anymore. Due to predictive coding in video, for example, the loss of data units generally has significant impact on the displayed sequence. Therefore, to provide a sufficiently high quality to the end user, we define the successful playout as only those events in which all data units can be decoded before their td,n . Therefore, with this abstraction for the multimedia application a receiver has basically only a single parameter to adjust its quality, namely the specification of the decoding time for the first data unit, td,1 .
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B. Problem Formulation – On-Demand Broadcast In a broadcast scenario, the receivers cannot explicitly issue a request to a streaming server, but the broadcast server distributes content without receiving any explicit requests. Commonly, users can only synchronously join the broadcast sessions by tuning in at a pre-determined receiving time and playout is started at some pre-determined playout time. Obviously, users could tune in at arbitrary time later into the ongoing broadcast session, if random access capabilities in the encoded media sequence are provided. This obviously results in the fact that late tune-in users miss some early content. To receive the entire content, broadcast servers might use a carousel, i.e., the multimedia stream is broadcast periodically. In this case receivers tuning in at arbitrary time might wait for the re-start of the sequence, which in turn might cause undesired playout delays. However, if the content is appropriately authored, e.g., a sequence of independent clips, the receiver might start displaying sequences out-of-order. However, for many ondemand applications this is undesirable. Do you want to know who the murderer was before getting all the tension and misleading hints? Are you interested in the final result of the world cup final before even knowing the line-up? Especially in wireless environments, where user mobility is one of the key aspects, receivers will enter a broadcast area asynchronously, i.e., at arbitrary time instants. So, in such hostile transmission environments with costly resources, the provision of high-quality on-demand services poses some challenges to be solved in the following. Specifically, we want to answer the following questions: • How can low-delay and efficient asynchronous media delivery be achieved in lossless broadcast environments? • How can reliability be achieved in asynchronous wireless multimedia transmission, where losses are inevitable? • Can these concepts be combined to obtain sufficient reliability, low delay, and efficient bandwidth exploitation? • What are the trade-offs between the initial playout delay and the successful playout, as a function of the receiving conditions for individual receivers? III. A SYNCHRONOUS M EDIA D ELIVERY A. Trivial Solutions As already mentioned, an obvious solution to provide media delivery with the option of asynchronous access is simply carouseling the entire media stream, i.e., the transmission of the first media unit starts from begining after the last media unit has been transmitted. This technique is also denoted as broadcast disk. The upper part of Fig. 3 visualizes the concept of a broadcast disk. To formalize the concept of asynchronous media reception in broadcast environments, we assume that receivers tune into an ongoing broadcast transmission, without any coordination between transmitter and receivers, and, without any coordination among receivers. Although a receiver subscribing to a broadcast disk can be lucky and receive the first data unit of the media stream of interest right away, in general the user has to wait for a significant amount of time until the receiver obtains the first data unit. In the worst case, the corresponding receiver just missed the first data unit, such that the worst case initial delay ∆ can be as large as ∆ = T , with T denoting the duration of the media stream1 . If such a long waiting time is not desired, the service provider has to accept that the transmission bandwidth must be expanded by some factor ω, e.g., by broadcasting the carousel with a bit-rate higher than the corresponding bit-rate of the media-sequence. However, this approach requires appropriate buffer space at the receiver, since the media consumption rate is lower than the reception rate. An alternative, but also a trivial solution, is to broadcast the same media stream on K parallel channels, as shown in the lower part of Fig. 3. On each channel a delayed version of the media stream is offered periodically with a delay shift of T /K. The transmission rate of each channel is selected to fit the media rate. This 1
We assume that the channel transmission rate corresponds to the media bit-rate.
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allows receivers to successfully playout the stream with a worst-case waiting time of ∆ = T /K, by tuning into the channel on which the media stream starts next. Compared to the previous scheme, the buffer requirements are relaxed, since in this case the media-consumption rate is equivalent to the channel transmission rate. Unfortunately, the overall required bandwidth (transmission rate) increases by a factor of ω = K, which makes this scheme very unattractive for wireless networks.
Fig. 3. Trivial solutions for asynchronous media streaming over broadcast channels: Broadcast disk and K parallel and shifted channels. The latter approach requires a bandwidth expansion of ω = K.
B. Advanced Broadcasting Techniques Due to this inefficiency of trivial broadcast disks, significant effort has been made to find more sophisticated pre-processing schemes, which allow asynchronous access with both lower initial playout delays and better exploitation of the provided bandwidth, see e.g., [1], [2] and references therein. Most of them have in common that the media bit-stream is segmented into segments Si , where each segment is periodically broadcast on different parallel channels, but each channel is assigned a different transmission rate Ri . The segments Si , with i = 1, . . . , S, are obtained by segmenting the total media stream into S segments, such that each segment contains a certain number of consecutive data units. Furthermore, we treat each segment as a super data unit in a sense that each segment gets assigned a segment size |Si |, representing the sum of the length of the contained data units, as well as a latest decoding time (for the segment) tDTS (Si ) representing the earliest decoding time of any contained data units in this segment. In [2], Harmonic Broadcasting (HB) has been proposed as a bandwidth-optimal solution for Constant Bit-Rate (CBR) media. Harmonic broadcasting suggests to segment the media stream into S segments of equal duration, where segment Si is broadcast with bit-rate Ri = RM /i, resulting in a bandwidth expansion S X 1 (1) ω= i i=1 and maximum waiting time ∆ = T /S, where RM denotes the bit-rate of the media stream. By varying the overall number of segments S, initial waiting time ∆ can be traded versus bandwidth expansion ω. A shorter waiting time yields an increase in bandwidth. However, compared to the trivial solutions the bandwidth expansion is moderate. Fig. 4 sketches the principle of HB. In this illustrative example the media bit-stream is segmented into S = 4 segments. As can be observed, each segment Si is broadcast with different transmission rate Ri
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Duration Media bit-rate
Media Sequence
Segmentation 1
2
3
Tune-in time Rx2
Tune-in time Rx1 Ch1
1
Ch2
2
Ch3 Ch4
Rx1
4
… … … …
3 4
Pre-buffering
playout
1
playout
playout
2
3
playout
4
Initial playout delay Rx2
playout
1
playout
2
playout
3
playout
4
initial playout delay
Fig. 4. Harmonic Broadcasting: The original media stream is segmented and segments are transmitted at different rates. Receivers can tune in at arbitrary time and retrieve the media stream from the beginning after a short waiting time. The spaces between segments are for illustration purposes only.
on individual channels (Ch1- Ch4). The individual transmission rates of the channels are indicated by different widths of the bars, resulting in different delivery times of the segments, e.g., segment S2 requires twice the time to be transmitted compared to the segment S1 . Two receivers (Rx1, Rx2) tune into the broadcast at different subscription time, i.e., asynchronously. The highlighted bars within the channels indicate the data which are exploited by receiver Rx1. After the first segment S1 has been completely received, the receiver starts the playout of the first media segment and immediately stops listening to the first channel. As can be observed, the structure of the scheme is such that after finishing the playout of segment Si it is guaranteed that segment Si+1 is completely available at the receiver buffer. The receiver immediately continues playing out segment Si+1 and releases listening to channel i + 1. Originally, this scheme was designed to operate in lossless environments only. However, since especially wireless networks suffer from losses, in the following we will discuss how HB can be extended to operate over lossy channels. Additional means for providing reliability based on the concept of fountain coding will be introduced. Unfortunately, fountain codes have originally been designed only for applications without delivery deadlines. We will discuss a scheme which combines HB with the fountain concept allowing asynchronous access to streaming content, reliability as well as scalability. IV. A SYNCHRONOUS F ORWARD E RROR C ORRECTION A. Fountain Codes on the Erasure Channel In order to understand the concept of asynchronous media streaming over lossy networks, first, we review the concept of asynchronous download based on fountain coding, which was introduced in [6]. The key difference between download services and streaming services is that streaming delivery must be accomplished i a such way that the media application at the receiver can make use of earlier received data
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units while the reception of later data units is still in progress. In contrast, download applications start processing the data after the entire file has been received. Early playout features are generally not foreseen or would be senseless, e.g., imagine the download of a binary executable, which cannot be executed if only received in part. Moreover, when download data is protected against packet loss by error correcting codes, a non-systematic code might be applied, which requires a successful decoding operation in order to reconstruct the original data. Fountain codes are a special class of FEC codes and were originally designed to allow asynchronous download over broadcast channels very efficiently. In general, FEC codes are employed in order to produce a set of encoding packets from a set of information packets. If a so-called systematic code is applied, one part of the encoding packets consist simply of the information packets itself and the other part consists of additional parity packets. In contrast non-systematic codes produce a set of encoding packets which in general do not contain a direct representation of the information packets. For ease of exposition we will consider systematic codes in the following. In a communications setup with FEC, both the information packets and the parity packets are transmitted over the network. Then, if packet losses (erasures) occur, the decoder located at the receiver might be capable to correct missing information packets by exploiting the additionally transmitted parity packets. Let k denote the number of information packets and g the number of produced parity packets, such that in total n = k + g packets are transmitted. Then, the corresponding code rate is defined by r = k/n. From coding theory it is known that codes can be constructed which allow the reconstruction of all information packets from any k out of n received packets. This property is also denoted as Maximum Distance Separable (MDS). Reed-Solomon codes, for instance, are a prominent representative of erasure codes which posses MDS property. Theoretically, any block length n can be supported by these codes by adjusting their finite field size. However, due to their high encoding and decoding complexity with increasing block length n, these codes are commonly only employed in practice for short to moderate block length n (e.g., n = 255).
…
TX RX 1 RX 2 RX 3 RX 4
Systematic packet
Parity packet
Erased packet
Fig. 5. Asynchronous download scenario over a broadcast channel. The transmitter (TX) encodes the information packets with a fountain code and broadcasts the potentially limitless number of encoding packets. Each receiver (RX) tunes into the ongoing broadcast session at arbitrary time and picks up sufficient packets. No feedback channel is required in order to request retransmissions.
Recently, in [6] the notion of rateless channel codes has been coined indicating that an infinite amount of parity packets n → ∞ from k information packets can be generated. Thus, the code rate r approaches 0, i.e., r → 0. These codes are also referred to as fountain codes. An ideal fountain code has the property that the entire source message can be reliably reconstructed from any k received encoding packets. Although ideal fountain codes with manageable decoding complexity have not been found so far, practical codes are capable of approaching the performance of ideal codes very well. Usually only a slightly increased number k 0 of packets has to be received. Practical fountain codes for packet loss channels, with reasonable
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encoding and decoding complexity, have for example been obtained by LT codes and Raptor codes [6] approaching ² , (k 0 − k)/k → 0. In the remainder we assume the existence of ideal fountain codes as exposition is simplified and the degradation when using practical codes is negligible. We would like to point out that the encoding/decoding complexity of the practical fountain codes can be kept quite low. The complexity analysis is, however, out of the scope of this study. In the sequel we outline the basic concept of the application of fountain codes to asynchronous and reliable data download in broadcast environments. For a detailed description we refer to [5], [6]. Fig. 5 illustrates an asynchronous broadcast scenario, consisting of one transmitter (TX) and multiple receivers (RX1 - RX4). The transmitter generates an infinite number of parity packets g = n − k → ∞ from k information packets. All packets are assumed to be broadcast consecutively. This allows receivers to subscribe to the ongoing broadcast session at arbitrary time and to collect any k packets not even in a consecutive manner. In lossy environments receivers just await k successfully received packets. Note that in this case receivers with good channel state, i.e., without any errors, only have to subscribe to k packet slots before being able to start reconstructing the information packets, whereas receivers experiencing losses have to wait longer according to their experienced channel conditions. With the digital fountain approach, no feedback channels from the receivers to the transmitter are required, as it is the case for retransmission schemes. Each additional packet is beneficial for the reconstruction of the information message. No receiver receives useless information. However, as pointed out in the previous subsection, decoding cannot be initiated before the last of the k required packets has been received. Therefore, this concept is only suitable for download-and-play applications, since it does not guarantee reconstruction of parts of the information packets on the fly, as required for streaming applications. B. Fountain Codes over Wireless Channels Fountain codes were originally defined and investigated on erasure channels only, since they were proposed to operate in networks which encounter packet losses, e.g., originating from packet dropping in congested routers. However, data packets transmitted over a wireless link, e.g., the air interface, are not entirely lost, but disturbed due to fading, interference, and noise. In contrast to packet-lossy networks, at least some noisy version of the original signal reaches the receiver in this case. Depending on the channel characteristics of the transmitter–receiver link, a weak or strong representation of the transmitted signal is observed at the receiver, i.e., the signal is faded. Additional disturbances are introduced by interference and thermal noise at the receiver. It is well-known from coding and communications theory, and already the state-of-the art for decoding of channel codes applied on the physical layer of communication systems, that it is always beneficial to include all available information into the decoding process. The so-called soft-information, i.e., a probabilistic measure whether an observed bit within a transmitted packet is a 0 or a 1, should be propagated from the signal detector to the FEC decoder. A wireless transmission system based on the digital fountain approach would add an additional fountain code on top of the physical layer channel code, in order to provide the features described in the previous subsection. Thereby the physical layer FEC decoder exploits the soft-information from the wireless channel and declares uncorrectable transmission packets as erased, such that the fountain decoder sees a virtual erasure channel. Such an approach is currently applied and foreseen for MBMS (multimedia broadcast and multicast services) over GERAN and UTRAN, as well as DVB-H, where Raptor codes with erasure decoding have been included into the standards, operating on above the physical layer. Declaring physical layer packets after decoding as erased is suboptimal and removes significant information. Moreover so called soft-out decoders can provide soft-information about each decoded bit, likewise to the signal detector. Starting from this point, recently in [7] soft-in decoding of fountain codes was discussed. It was shown that reasonable performance gains can be achieved if the soft output of the physical layer detector or channel decoder is forwarded to a soft-in fountain decoder, instead of erasure decoding. It was shown that even better performance can be achieved if no physical layer channel code
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is applied at all, but the two codes (physical layer FEC and fountain code) are combined into one soft-in fountain code, i.e., the soft-information from the channel is directly passed to the fountain decoder. Specifically in [7], the concept of fountain coding has been investigated on other than erasure channels. A decoding algorithm for LT codes which exploits soft information was investigated. Furthermore, a theoretical performance limit of fountain codes on general channels has been introduced, i.e., the Asymptotically Optimal Fountain Code (AOFC). The probability po (k, n) that the fountain decoder cannot decode the k information packets after listening to n packets can be estimated quite well by collecting soft information appropriately. In this case, the received information for each packet is not binary in a sense that the packet is fully received or completely lost, but basically each packet contributes to the collection of the entire information. Less noisy packets contribute more to the collection process whereas packets experiencing heavier noise contribute less. The mutual information can be used as a quite good estimate on how much a certain packet contributes to the reconstruction of the entire message. The results haven shown that practical codes can operate quite close to an AOFC and, furthermore, soft decoding of fountain codes provides significant gains compared to conventional schemes as explained previously. Finally, it is worth to mention that this concept generalizes the idea of fountain codes and includes erasure channels as a special case. V. A SYNCHRONOUS W IRELESS S TREAMING A. Rateless Broadcasting for Asynchronous Reception In the following the combination of asynchronous media delivery with asynchronous FEC (i.e., fountain coding) will be discussed, in order to obtain a reliable, bandwidth efficient, and delay-scalable wireless on-demand system. Consider a media stream which is segmented into segments Si as introduced in Section III-A by any appropriate algorithm [1], [2], [8], e.g., HB. Then, each segment Si containing |Si | information bits is encoded with an individual fountain code Fi , i.e., Xi = Fi (Si ), in order to obtain a potentially limitless amount of parity data for each segment. Bits from the bit-stream Xi which is called here the segment fountain, are mapped on packets and packets of each segment fountain Xi get assigned an individual transmission rate Ωi in packets per time unit. We assume that the overall transmission rate P Ω = Ω. Ω is shared among different segments by any orthogonal multiplexing method such that i i We restrict ourselves to time-sharing in the remainder. Therefore, segment fountains Xi are alternatingly mapped to transmission packets with weights according to their rate Ωi . The multiplexing period α determines the number of transmission slots such that within one period a certain segment gets assigned αΩi /Ω transmission slots. Fig. 6 visualizes the principle of the scheme, exemplary for HB. Again the transmission rate of each channel is indicated by the width of the bars. The highlighted regions show the data observed by the receiver. The yellow boxes indicate the minimum amount of data required by each fountain decoder to reconstruct the segments. As indicated in the figure, packet loss might require listening longer than indicated by the yellow boxes to individual channels. However, due to the marvelous properties of fountain codes, each additional packet is beneficial for the decoder and is able to compensate one lost packet. The dotted lines indicate the time instances where the corresponding segments are reconstructed, i.e., fountain decoding of the corresponding segment is successful. The figure shows two playout approaches with two different initial delays, ∆1 and ∆2 . As can be observed, if ∆1 is selected segment S3 cannot be reconstructed on time, resulting in a playout interruption at the receiver. In contrast, if ∆2 is selected, each segment can be reconstructed before exceeding its individual deadline and fluent presentation of the media-sequence at the receiver is achieved. For successful decoding at the receiver it is not important which packets have been received, but only that sufficient information for each fountain is collected. Hence, this scheme provides two features at the same time, namely (i) full reliability can be achieved if the receiver just waits long enough until sufficient information is collected, and (ii) asynchronous reception is realized by the properties of fountain codes. The playout delay for each user can be adapted to its actual channel characteristics such that scalability in terms
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of delay is realized. Note that with this scheme the only distortion that can occur is playout interruption, in contrast to traditional streaming systems where individual lost frames may result in presentation errors and error propagation. Lost data
1
Ch1
2
Ch2
3
Ch3 Ch4
4
Playout interrupted: service outage playout
1
playout
2
playout
3
playout
4
playout
3
playout
4
1
2
playout
1
playout
2
Fig. 6. After segmentation of the media stream, each segment Si is encoded with an individual fountain code Fi . Each segment fountain Xi is transmitted at different rate Ωi on channel Chi. Reliability of presentation can be increased by increasing the initial delay ∆.
B. Service Outage In order to allow proper presentation of the media at the receiver, the reconstruction of each segment Si should take place before its reconstruction deadline, tRTS (Si ), expires. However, the receiver obviously wants to start decoding and presenting the first segment Si although the remaining segments are still being received. Hence we are interested in how long a receiver should delay the presentation of the first segment in a lossy environment in order to achieve a desired service reliability. To answer this question, we assume that the receiver is at least aware of its average receiving conditions, represented by some channel statistics, e.g., the loss probability of radio blocks or the distribution of the SNR. Furthermore, we have to assume some stationarity of the channel during the reception time of the sequence. Without loss of generality, let us assume that the receiver tunes into the ongoing broadcast session at tr = 0. Furthermore, assume that the decoder decides for a playout delay ∆: Then, with segment decoding time-stamp tDTS (Si ), the latest time each segment fountain Xi must be reconstructed to avoid a playout problem is tRTS (Si ) = tDTS (Si )+∆. Furthermore, let Ni (t) denote the number of packets transmitted from segment fountain Xi up to time t. We assume that once the receiver subscribes to the broadcast session, it stays tuned and, therefore, the receiver likewise listens to Ni (t) consecutive packets up to time t. Then, the probability po,Si that segment Si cannot be reconstructed in time is po,Si = po (|Si |, Ni (tRTS (Si ) = tDTS (Si ) + ∆)). Therefore, for stationary channel statistics, the probability POUT (∆) that at least one segments cannot be reconstructed in time, and hence the media sequence cannot be played out successfully is POUT (∆) = 1 −
S ³ Y
¡ ¢´ 1 − po |Si |, Ni (tDTS (Si ) + ∆) .
(2)
i=1
Assuming that the receiver is aware of the channel statistics, the media segmentation Si , the segment decoding time stamps tDTS (Si ), and the transmission rates Ωi , each receiver can decide how long to wait in order to achieve a sufficiently small service outage probability POUT (∆). Alternatively, the calculation
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can be performed at the transmitter in advance for a set of channel parameters [9] and be made available within the setup of the broadcast session. However, in contrast to other scalable systems, the play-out quality is the same, only the initial delay is different for different receiving conditions.
required initial playout delay ∆ in sec
VI. P ERFORMANCE E VALUATION
ω=4.2, Soft ω=4.2, Hard ω=3.5, Soft ω=3.5, Hard
2000
1500
1000
500
0 0
2
4
6
ES / N0 in dB
8
10
Fig. 7. Performance results on the AWGN channel: Initial delay ∆ vs. ES /N0 for soft-decoding and erasure-decoding.
In order to validate our analytical findings, we evaluate (2) for Harmonic Broadcasting (HB) [2]. We consider a CBR media stream with data rate RM = 128 kbit/s and duration of T = 2 hours. HB was shown to be bandwidth-optimal for CBR media [2] and is therefore also used for our purposes. The bit-stream d for both S ∈ {18, 36}. is segmented in segments S of equal size, i.e., ∀i |Si | = k. We select k = T ·R S The packet size is selected as M = 20 bytes, the multiplexing period α and Ωi as α = (|S| · H(S))/M P and Ωi = 1/(i · H(S)) respectively, with H(n) = ni=1 1/i. This results in a bandwidth expansion of ω = 3.5 for S = 18 and ω = 4.2 and S = 36, respectively. The transmission time interval ttti between two consecutive packets is set to ttti = M/(RM · H(S)). We consider transmission over the AWGN channel, where receivers experience different receiving conditions expressed by different Signal-to-Noise Ratios (SNR), ES /N0 . Two modes of operation are compared. The first mode investigates the performance when erasure based decoding of the segment fountains is considered. For this, appropriate erasure declaration of packets is required, i.e., radio blocks are declared as erased at the receiver if residual bit errors within a radio block are detected. In the second scheme, erroneous radio blocks are not declared as erased, but soft information about each bit is propagated to the Fountain decoder. For a more detailed description of the implementation of such decoders we refer to [7]. It is also shown in [7] that practical codes perform almost as well as asymptotically optimal codes. The following results are based on the latter. We evaluate (2) for a different ES /N0 and determine the required initial playout delay ∆ such that the resulting outage probability is arbitrarily small, i.e., POUT < 10−6 . Figure 7 shows the required initial delay ∆ over ES /N0 in dB. Basically, the following conclusions can be drawn. First, with decreasing
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ES /N0 the required initial delay increases. This comes from the fact that more time is required in order to allow successful reconstruction of every segment. Second, with increasing bandwidth expansion, i.e., with increasing number of segments, the initial delay can be reduced. Soft decoding outperforms the erasure based scheme and reduces the required ∆ in the lower ES /N0 region significantly. In case that no losses are present (for high ES /N0 ), a minimum initial delay resulting from the broadcasting scheme itself is still required. Moreover, in this region benefits from soft decoding cannot be obtained. This allows receivers to decide whether the more complex soft decoding or the less complex erasure decoding should be applied based on their channel state. The selection of the decoding algorithm is performed at the receiver only, i.e., the transmitted data is equivalent in both cases. In the lower SNR region, the receiver might decide on available computational resource if soft decoding can be applied. Complexity evaluations are out of the scope of this work. VII. C ONCLUSIONS We considered wireless broadcasting of multimedia content with asynchronous media access. Receivers subscribe at any time to the ongoing broadcast session, but are still able to display the media stream from the beginning. Thereby, the media stream is segmented and each segment is protected by a fountain code. Each encoded segment is broadcast at different rates. The presented framework allows asynchronous data reception as well as full reliability at the same time. Furthermore, error control is fully shifted to the receiver side and is achieved by the delayed media presentation. The presented scheme is fully scalable and allows an arbitrary number of heterogeneous receivers. Finally, analytical expressions have been presented which allow us to estimate the probability of non-successful media playout, i.e., an interruption in the presentation. This allows receivers to adjust a desired service reliability by delaying the playout for a sufficient time. Performance results were presented showing the practicality of the discussed framework and possible performance gains by soft decoding compared to erasure decoding. R EFERENCES [1] A. Hu, “Video-on-Demand Broadcasting Protocols: A Comprehensive Study,” in Proc. IEEE Infocom 2001, Anchorage, Alaska, Apr. 2001. [2] L. Engebretsen and M. Sudan, “Harmonic Broadcasting is Bandwidth-Optimal Assuming Constant Bit Rate,” in Proc. Annual ACM-SIAM Symposium on Discrete Algorithms 2002, San Francisco, CA USA, Jan. 2002. [3] L. Xu, “Efficient and scalable on-demand data streaming using uep codes,” in Proc. ACM International Conference on Multimedia 2001 (MM’01), Ottawa, Ontario, Canada, Sept. 2001. [4] C. Huang, R. Janakiraman, and L. Xu, “Loss-resilient media streaming using priority encoding,” in Proc. ACM International Conference on Multimedia 2004 (MM’04), New York, NY, USA, Oct. 2004. [5] M. Mitzenmacher, “Digital fountains: A survey and look forward,” Proc. of the IEEE Information Theory Workshop 2004, San Antonio, TX, USA, pp. 271–276, Oct. 2004. [6] J. Byers, M. Luby, and M. Mitzenmacher, “A digital fountain approach to asynchronous reliable multicast,” IEEE Journal on Selected Areas in Communications, vol. 20, no. 8, pp. 1528–1540, Oct. 2002. [7] H. Jenkac, T. Mayer, T. Stockhammer, and W. Xu, “Soft Decoding of LT-Codes for Wireless Broadcast,” in Proc. IST Mobile Summit 2005, Dresden, Germany, June 2005. [8] A. Mahanti, D. L. Eager, M. K. Vernon, and D. J. Sundaram-Stukel, “Scalable on-demand media streaming with packet loss recovery,” IEEE/ACM Transactions on Networking, vol. 11, no. 2, pp. 195–209, Apr. 2003. [9] T. Stockhammer, H. Jenkac, and G. Kuhn, “Streaming video over variable bit-rate wireless channels,” IEEE Trans. Multimedia, vol. 6, pp. 268–277, Apr. 2004.
