Reducing Channel Zapping Delay in WiMAX ... - Semantic Scholar

Reducing Channel Zapping Delay in WiMAX-Based IPTV Systems Alireza Abdollahpouri1,2 and Bernd E. Wolfinger1 1

Department of Computer Science - TKRN University of Hamburg, Germany 2 University of Kurdistan, Sananda, Iran [email protected]

Abstract. Due to the enormous improvement of networking technologies and the advances in media encoding and compression techniques, IPTV becomes one of the fastest growing services in the Internet. When offered via wireless technologies (e.g., WiMAX), IPTV can pave the way for quad-play in next generation networks and ubiquitous delivery. IPTV subscribers expect the same or even better Quality of Experience (QoE) compared with the services offered by traditional operators (e.g., cable or satellite). An important QoE element is the channel switching delay also known as zapping delay. In this paper we propose a prediction-based prejoin mechanism to join one or two TV channels (which are likely to be selected next) in advance in order to shorten the channel switching time in WiMAX-based IPTV systems. A trace-driven simulation is conducted to evaluate the proposed method. The simulation results confirm the reduction of about 30% in average zapping delay. Keywords: IPTV, Zapping delay, WiMAX, Performance evaluation.

1

Introduction

Traditional one-way broadcasting of TV programs no longer satisfies the new generation of TV users which have grown up with Internet and interactive gaming. Internet Protocol TV (IPTV) describes a mechanism for transporting TV streams encapsulated in IP packets using networking protocols and tries to offer more interactivity and more control over the content. To provide ubiquitous delivery, IPTV service providers have to pay special attention to wireless broadband technologies as their access networks. Worldwide Interoperability for Microwave Access (WiMAX) technology which is based on IEEE 802.16 air-interface standard has salient features like high data rate, multicast support, guaranteed quality of service and scalability. Therefore, it can be a good candidate to deliver IPTV services to fixed and mobile subscribers. The time between pushing the channel change button and the first video frame being displayed on the TV, is called zapping delay. Besides the acceptable audiovisual quality, channel zapping delay is a fundamental requirement for quality of user’s experience (QoE). Although it seems to be a natural requirement from a subscriber's perspective, providing this functionality can be problematic for network operators. J.B. Schmitt (Eds.): MMB & DFT 2012, LNCS 7201, pp. 182–196, 2012. © Springer-Verlag Berlin Heidelberg 2012

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Recently many research efforts have been devoted to reduce channel zapping delay [1]-[18]. Cisco proposed VQE (Visual Quality of Experience) appliance for xDSLbased IPTV systems, which uses I-frame caching and unicast bursts to accelerate channel switching time [1]. J. Lin et al. in [2] proposed another unicast-based method which distributes an additional media stream starting with a key frame (to reduce I-frame acquisition delay). However, because of the correlation in channel switching events (e.g., during commercial advertisements), unicast-based schemes lead to spikes in the network load. In order to prevent such an impulsive load, [3] and [4] used multicast-based approaches (using an additional multicast stream instead of using an additional unicast burst). This additional stream can be a time-shifted copy of the original stream (in [4]) or a secondary lower quality (only I-frame) stream [3]. In [5] H. Joo et al. proposed a method to insert extra I-frames into the channels based on the user’s channel preference information. However, this method reduces the compression efficiency. H. Uzunalioglu in [6] tried to adjust the GOP (Group of Pictures) duration to decrease channel change delay. All of the abovementioned techniques try to reduce I-frame acquisition delay. Prejoining or predictive tuning is another technique in which one or more channels (those which are likely to be selected next) are prejoined and pre-buffered in addition to the currently watched channel in order to reduce channel zapping time [7]-[11]. In [7], U. Oh et al. presented various hybrid channel prefetching and reordering schemes and showed that the adjacent channel prefetching scheme has better performance than the popular channel prefetching scheme no matter what reordering scheme is used. A rating server is proposed in [8], which gathers information about channel change events from Set-Top Boxes (STBs) and manages statistics for each STB (which, of course, could lead to privacy problems). Based on those statistics a list of channels is predicted and therefore, the user experiences low zapping delay when selecting those channels. A survey of prediction-based methods to reduce channel zapping is presented in [9]. In [10] and [11], the authors try to predict the channels based on the surfing behavior of IPTV subscribers. Scalable Video Coding (SVC) schemes can also be applied for rapid channel switching. In [12] Y. Lee et al. allocated the base layer and enhancement layer of each channel to two separate multicast groups. The base layers of a collection of candidate channels are stored in the buffer. Channel switching in preview mode occurs immediately since the users access the base layers without delay. In watching mode, both the base and enhancement layers of the selected channel are used to achieve full quality. A combination of prejoining and SVC to reduce zapping delay is used in [10]. Some of the researchers try to influence network factors such as latency in the access network by enhancing IGMP features (e.g., reducing number of IGMP membership queries, join before leave and snooping). In [13] authors proposed sending an IGMP-Join message for the requested channel before leaving the currently watched channel by sending an IGMP-Leave message. E. Lee et al. in [14] proposed a new extended IGMP for mobile WiMAX access networks. Fig. 1 summarizes some of the most important methods used for reducing channel switching delay.

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In this article, we try to reduce the average switching (zapping) delay by mixing two mechanisms: a combined multicast/unicast transmission of TV channels and prediction-based prejoining. Taking advantage of multicast support of WiMAX and considering the minimum slot requirement, one or two channels that are likely to be selected next, will be prejoined. Prejoining is only applied for unicast channels and in surfing period. The main differences between this work and other prediction-based methods are twofold: we focus on WiMAX networks while other works are for DSLbased access networks and we combine prediction-based prejoining with multicasting most popular channels to reduce zapping delay. Note that, in our paper, prejoining is not in the sense of multicast join but in the sense of pre-requesting. The rest of this paper is organized as follows. In Section 2 some background information about Multicast Broadcast Service (MBS) in WiMAX and channel switching delay is given. Section 3 presents our prediction-based prejoin proposal. We then present our simulation-based performance evaluation of the proposed method in Section 4. Finally, we conclude the paper in Section 5.

Fig. 1. Categorization of zapping delay reduction schemes

2

Background

2.1

WiMAX Multicast Broadcast Service (MBS)

Unicast transmission may not be an efficient approach in terms of bandwidth requirement, because the resource requirement increases proportionally with the number of users. Based on the useful features of similar technologies (namely MBMS, DVB-H and MediaFLO), the IEEE 802.16e standard proposed Multicast Broadcast Service (MBS). MBS provides an efficient method for concurrent transmission of commonly demanded data (e.g., a TV channel) to a group of users, using a common multicast connection identifier (MCID).

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Using MBS, bandwidth requirement reduces from one burst per viewer to one burst per TV channel. MBS is offered in the downlink only. To manage overall operations of MBS, an MBS controller (server), is needed in the system as shown in Fig. 2. It is worth noting that MBS can not take advantage of Adaptive Modulation and Coding (AMC) because it must fulfill the requirements of all the clients in the multicast group (even those in the border of the cell with low SNR). Meanwhile, ARQ can not be used in multicast sessions.

Fig. 2. WiMAX access network with MBS server (ASN-GW: Access Services Network Gateway; BS: Base Station; MS: Mobile Station; SS: Subscriber Station; STB: Set-Top Box)

The BS sends an MBS MAP (media access protocol) message to specify the location and size of MBS data bursts in the MBS region of the downlink subframe. MBS MAP is located at the first subchannel and the first OFDMA symbol of the MBS region as illustrated in Fig. 3. Similar to unicast services in IEEE 802.16, the MBS service flows are managed through a DSx (Dynamic Service Addition/Deletion/Change) messaging procedure used to create, change, and delete a service flow for each MS.

Fig. 3. Multicast and unicast in a WiMAX cell

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A WiMAX cell with seven IPTV users is depicted in the Fig 3. The numbers beside the stations indicate the TV channel each user is already watching (at an instant of time). Three users are watching channel 1 and two others are watching channel 2. TV channels 3 and 4 are watched by only one user. Channels 1 and 2 are transmitted by means of multicast using multicast bursts. Unicast bursts are used for TV channels 3 and 4. The number of data bursts (unicast and multicast) is equal to four. Note that because channel 3 is watched by a user which is close to the BS (good signal condition), the resource requirement (number of OFDMA slots) is lower. 2.2

Channel Switching Delay in IPTV Systems

In the analog cable TV network, all the channels are available simultaneously on the link with different frequencies. Therefore, channel change is almost instantaneous, since it only involves the TV receiver tuning to a new carrier frequency and then demodulating the analog signal and displaying the video on the screen. With the introduction of digital transmission technologies and video compression techniques such as MPEG, channel change is no longer immediate, because of some factors like I-frame acquisition delay, de-jitter buffering delay and MPEG buffering delay. In other words, the dependency on previous frames in compressed video streams prevents the ability of random access and prolongs the switching delay. Changing a channel in an IPTV system is even more complicated due to addition of further delay components such as multicasting delay (IGMP join and leave and route establishment) and buffering delays in the intermediate nodes of the network. The steps involved with channel switching depend on the networking infrastructure and the location of the requested channel. For instance, if the channel is available at the BS, then the delay is shorter than for the case when the channel must be requested from the MBS server. In an IPTV system, due to the limited capacity of the last-mile, a rather limited number of TV channels can be transmitted. Therefore, one could experience a switching delay of about a few seconds. Switching to an MBS channel, involves a shorter delay in comparison to switching to a unicast channel. Because in the former case, channel switching time only consists of the delay to wait for the next burst on the target MBS stream and buffering it in the MS to avoid underflow and remove the jitter. In the latter case however, the MS should at first obtain bandwidth to transmit the channel change request (in MAC and IP level). This is a random backoff-based contention mechanism and happens during the contention period in the uplink subframe. If the channel change request is accepted by the BS and after scheduling the requested channel, BS advertises the position of the unicast bursts via DL-MAP. Thereafter, the MS should decode the MAP and find the location of the desired burst. Finally, after a buffering delay, the first picture of the new channel is displayed. Note that the optimization techniques that have been explored in the wired IPTV domain (e.g., a separate tune-in stream or prediction prejoining) can also be adapted for WiMAX MBS. To summarize, zapping time can be influenced by many factors such as: • Multicast latency for “leaving” the old channel and “joining” the new channel; • Program Clock Reference (PCR) and sequence header information;

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• Random access point (such as I-frame) acquisition delay (example: If GOP=15 and fps=30 then an I-frame is produced every 500 ms); • Network buffer delays, including delays caused by error-mitigation techniques; • MPEG decoder buffer delay. Note that the ITU-T FG IPTV group is recommending that the time taken by the channel switching process should not exceed 2.5 s.

3

Workload Generation and Prediction-Based Prejoin Method

Before turning to our proposed method, we need some knowledge about the workload and therefore we model the switching behavior of a typical IPTV user. 3.1

Modeling the Behavior of a Single IPTV User, during an ON Session

The behavior of an IPTV user is different from the users of other IP-based applications. Fig. 4 illustrates the typical behavior of an IPTV user during an ON period (active session). In this figure, switching events performed by the user during the peak hour (here, 9PM to 10PM) are depicted. A switching event occurs when a user selects a new TV channel. Several switching events with a certain inter-arrival time (e.g., less than 10 sec) show that the user is zapping TV channels to find something of interest. The number of channel switching prior to viewing is called a zapping block. For example, three zapping blocks with the size of 4, 2 and 6 can be seen in the figure during the one-hour time interval. Note that, the channels being actually watched during a long time period are not included in the zapping block. The user experiences a sequence of zapping periods followed by viewing periods. The user is in watching state whenever there is no switching event during a relatively large amount of time. Modeling and analyzing this different type of workload can help the IPTV service providers in the design process or after the implementation process to evaluate several “what-if” scenarios.

Fig. 4. Channel zapping (z) and viewing (v)

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Depending on the next channel chosen, channel switching is considered to be sequential or targeted. In the sequential channel switching, the user chooses the next channel using UP and DOWN buttons on the remote controller. Targeted switching represents the cases in which the user chooses the desired channel directly by pressing the channel number or using Electronic Program Guide (EPG). During a commercial advertisement or, e.g., between half-times of a football match, most users change the channel to find a more favorite program. In other words, channel switching behavior of the viewers may be correlated. There exist some problems to be considered in the modeling of the user behavior. In general, the following three main questions should be answered.  Q1. How many channels a user surfs in average before viewing? (Size of zapping block)  Q2. Which channels is a user watching or surfing? (Access pattern - Channel number)  Q3. When do channel change events happen? (Channel dwell time in viewing or zapping modes) In [19] we introduced our model to cover both, channel popularity and user activity, in an IPTV system for a single typical IPTV user. The proposed user behavior automaton (TV-UBA) to model zapping and viewing periods of Fig. 4 is depicted in Fig. 5. According to our model, after turning the STB on (State Si), the subscriber starts zapping channels with the probability of pz or watches a channel with the probability of 1-pz. In zapping mode, the user may surf one or more channels before viewing (zapping block). Each state indicates the number of successive channel switching events. For example, state Z3 means surfing three channels before watching. The user returns back to viewing mode after each zapping block. In viewing mode, after watching a specific channel, the viewer may terminate watching with probability pt, view another channel or start surfing another set of channels. Interested readers can refer to [19] for more detailed information about view and zapping states.

Fig. 5. User behavior automaton for IPTV user (TV-UBA), cf. [19]

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We used the LoadSpec tool [20][21] to formally describe and thereafter simulate our model. A sample output of our model is shown in Fig. 6. Zapping blocks and viewing time are clearly distinguishable. In this figure, the user first surfs three channels (zap block with size three) and then starts watching the fourth channel. After termination of the corresponding viewing period, the user browses five channels and finds the sixth channel of interest.

Fig. 6. Output of the TV-UBA model in LoadSpec tool [19]

3.2

Our Proposed Prediction-Based Prejoin Method

M. Cha et al. in [22] reported that about 60% of channel changes are sequential. In other words, more users prefer to switch channels using UP and DOWN buttons on the remote control. Therefore, in sequential switching, the next requested channel would be an adjacent channel. The rest of the switching events are called targeted switching, in which choosing the next channel only depends on the watching probability of the destination channel. Based on the above information, instead of just prejoining neighboring channels we propose a more intelligent prejoin method (to prejoin one or two channels) which considers the channel switching behavior of a typical IPTV user, as follows (the currently requested channel is Ci):

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where: p(Ci): watching probability of TV channel Ci. pta: Probability of targeted switching pseq: Probability of sequential switching (about 0.6 [22]), where pseq=1- pta pu: Probability of sequential UP switching (about 70% of sequential switching events). Here, Prejoin1 is a method which predicts the next channel based on statistics obtained from switching behavior of users in a real IPTV system. The probability state diagram for Prejoin1 is given in Fig. 7. In the figure, Cmax defines the maximum number of offered TV channels. Whenever Ci+1 is more than Cmax, evidently Ci+1 has to be replaced by C1 (we omit this in the text in favor of better readability). Prejoin2 always predicts the next upper channel for prejoining. Therefore, one or two channels will be prejoined depending on whether the prejoined channels are the same or not. Also, with probability pta⋅p(Ci) the method Prejoin1 will not recommend any channel to prejoin, in which case, too, only channel Ci+1 will be prejoined (according to method Prejoin2). Note that, prejoining is performed only for unicast channels.

Fig. 7. Prejoin1 (Ci) probability state diagram; Cmax=50

Let’s take a simple example to explain the prediction-based prejoin method. In Fig. 8, a part of the trace for an IPTV user is shown which consists of two zapping blocks and one viewing period. The numbers above the arrows indicate requested channels. For the sake of simplicity, assume channels are sorted by descending order of popularity (channel one is the most popular channel). If, for example, the five most popular channels are transmitted by means of multicast, the user experiences a shorter switching delay for these channels. Taking this example and assuming the scenario illustrated by Fig.8, Table 1 indicates the prejoined channels as well as channel change delay in each switching event (“T” represents long delay and “t” is used to indicate short delay). If the requested channel is correctly predicted and prejoined, the switching delay is virtually zero (at the time of switching the new channel is already buffered and ready to decode). Note that the first channel switching (if unicast) suffers a long delay since there is no prediction mechanism yet. For the third channel switching (channel 22), switching delay is zero because this channel is predicted and prejoined previously (by Prejoin1). This is also happening in the 8th switching event when both prejoined formulas predict the next channel correctly (in this case only one channel is prejoined which is channel 11).

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To save valuable bandwidth, the prejoined channels will be released in viewing mode (when channel dwell time is longer than 30 seconds). Therefore, at the 7th event, although the channel 10 is predicted and prejoined correctly, because of staying in viewing mode in the previous channel, the switching delay is long.

Fig. 8. Channel switching events in a period of time

For this example the average zapping delay is equal to:

Average_Zap_delay = (6T+2t)/10 Table 1. An example of zapping delay reduction with prejoining

4

Performance Evaluation

A trace-driven simulation is conducted to evaluate the performance of the proposed prejoin method. For this purpose, a dedicated simulation program was implemented by us using C++ programming language. Watching probability of TV channels can be modeled quite realistically using a Zipf-like distribution with the following formula: p(Ci ) =

Ω, iα

where

 C 1  Ω =   α   j =1 j 

−1

;

0 < α ≤ 1;

Here, α is the shaping parameter, Ω is the scaling parameter, which can be decided by α, and C is the number of TV channels. We assume 50 TV channels and α =1 [5], then: Ω = 0.2222622. The simulation scenario is similar to Fig. 2 which is composed of a WiMAX cell and 30 (and 60) IPTV users. The MBS server has access to all channels and handles the TV requests of IPTV users on behalf of the IPTV head-end. We elaborated the effects of overhead slots in WiMAX-based IPTV systems in [23] (using both analytical and simulation methods) and showed that with a correct

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combination of multicast and unicast TV channels and a proper scheduling policy, the overhead can be significantly reduced. The total number of required slots (data bursts plus overhead slots) for the cases of 30 and 60 users is shown in Fig. 9. For the case in which 30 IPTV users exist in the cell, multicasting the 4 most popular channels can lead to minimum slot requirement. When 60 users are served, the 8 most popular channels should be transmitted by means of multicast to expense the minimum number of slots when providing the IPTV service. Usually one MBS burst contains several GOPs and therefore, there will be more than one I-frame in the MBS burst. Hence, for WiMAX MBS we don’t need to account for an I-frame acquisition delay. We perform the simulation for the cases where the first N channels are transmitted by means of MBS (varying N=1, …, 10). We also assume target switching delay of 2.5 sec for unicast (T=2.5) and 0.5 sec for multicast (t=0.5). For each user, a 10-hour trace is obtained from an existing TV-UBA model. Note that, we use the term “trace” in its general sense, i.e. not only referring to measured data but also to data obtained from our TV-UBA model. Each entry of the trace includes the following information: ⋅ ⋅ ⋅

Timestamp Type of event (i.e., Request for channel- Start watching - Terminate watching) Number of requested channel (when event is Request for channel)

Fig. 9. Required slots when multicasting N most popular channels and unicast the rest of the requested channels (N= 1, …, 50) [23]

The flowchart of the simulation is depicted in Fig. 10. The average zapping delay with and without prediction is calculated for each user. The results are shown in Fig 11. The improvement in average zapping delay is between 33% and 28% for multicasting one to ten most popular channels as shown in Fig. 11. Considering the minimum overhead and slot requirement (which means that, here, multicasting the

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top 4 channels is the optimum decision), an improvement of about 30% in average channel zapping delay is obtained. We repeated the simulation for different user behaviors (different traces) and obtained similar results. Total delay reduction is a combination of the following two factors (Fig. 11): (a) Reduction obtained from multicasting N most popular channels (efficiency of multicasting) (b) Reduction obtained from prejoining mechanism (efficiency of prejoining) As can be seen, the efficiency of multicasting increases with the increment of the number of multicast channels and this clearly makes sense. But for the efficiency of prejoining, a gradual decrement can be seen. To calculate the percentage of successful prejoins, we analyzed the switching behavior of a typical user and the predicted channels obtained by Prejoin1 and Prejoin2. The results are as follows: ⋅ Total number of switching events (in a 10-hour trace): 341 ⋅ Number of prejoining channels (both Prejoin1 and Prejoin2) = 126 ⋅ Number of successful prejoins: 58 Therefore, the percentage of successful predictions is about 46%. We repeated the analysis for some other user behavior patterns and this value is almost the same.

Fig. 10. Simulation flowchart

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Fig. 11. Average zapping delay with and without prejoining (95% confidence intervals)

5

Conclusion

One of the most important challenges in IPTV systems is reducing channel change time or zapping delay. In this paper, based on our previous works on modeling switching behavior of IPTV users and slot requirement for different multicast/unicast combinations, we proposed a prediction-based prejoin mechanism to shorten channel zapping delay during surfing periods. Taking advantage of Zipf-like distribution of the watching probability of TV channels and multicast support in WiMAX networks, two mechanisms were successfully used to reduce channel zapping delay: combined multicast/unicast transmission of TV channels and prediction-based prejoining. Simulation results confirm that with the consideration of minimum slot requirement, quite significant improvements of about 30% in zapping time can be obtained. A combination of the other methods described in Section 1 with the proposed method in this paper can be used to shorten the zapping delay even more. For example, WiMAX can take advantage of scalable video coding in which the base layer can be transmitted with the most robust MCS and enhancement layers are transmitted with the less robust (but with higher data rates) MCS. In surfing mode, only the base layer can be prejoined and after dwelling in a specific channel for e.g., 30 seconds, a full quality can be achieved. A dynamic scenario can be investigated as a future work in which the arrival and departure rate of the subscribers are taken into account. Furthermore, the measurements obtained from a real IPTV system can also be used instead of the output of our TV-UBA model. Also, it would be of interest to implement our prejoin method in an existing IPTV system in order to quantify the resources which have to be spent (e.g., network bandwidth and STB CPU capacity), to achieve the performance improvements. Note that, although we have investigated WiMAX-based

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access networks in this article, the method can be applied in a straight-forward manner to other OFDMA-based wireless environments which support multicasting (e.g., 3GPP LTE).

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