Planning and Managing the IPTV Service
                        Dakshi Agrawal, Mandi...
by infrastructural changes). Our framework can be extended to
broadband Internet access. Typically a VSO would serve a           viewer characteristics. In this section, we first presen...
reach of a community may vary and a special case of it,
   Problem 3.1: Community Allocation Problem Given an             ...
viewing probabilities
                      viewing probabilities                                                         ...
There are data servers to support both the operational and
                                        data server            ...
is not presented here, if:                                          viewing experience from bad (1) to excellent (5). MPQM...
•   Server capacity: This is an easy parameter to handle since
Existing Communities                                                                      New Communities
  Community Id j...
Zapping delay   Video quality   VOD blocking prob.
                                                           (sec)       ...
Upcoming SlideShare
Loading in …5

Planning and Managing the IPTV Service Deployment


Published on

  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Planning and Managing the IPTV Service Deployment

  1. 1. Planning and Managing the IPTV Service Deployment Dakshi Agrawal, Mandis S. Beigi, Chatschik Bisdikian, Kang-Won Lee IBM T. J. Watson Research Center Hawthorne, NY, USA {agrawal,mandis,bisdik,kangwon} Abstract— The deployment of converged services of TV, tele- demand (VoD), and enhanced user experience with integrated phony, and Internet access over IP entails a significant initial listings for live and VoD programming, program navigation investment for service providers. This investment is not only for and search, VCR-like commands for VoD content and live the underlying network infrastructure but also for provisioning and managing vast data centers needed to provide sophisticated TV channels, and picture-in-picture features [1]. IP-based TV experience (IPTV), including multiple camera views, Rolling out the IP-based video service is a major under- live (broadcast) and on demand programming, etc., to a large taking to the service providers in terms of effort and cost number of viewers. Thus the service providers need to carefully due to a huge initial infrastructure cost and ongoing service plan and manage the IPTV service deployment to maximize the return-on-investment while providing a good quality of management. Here, the infrastructure does not only include the experience to the subscribers. In this paper, we develop a communications infrastructure (such as routers, switches, and methodology to aid service providers to effectively plan for the links) but also the facilities in the data centers, such as servers staggered deployment of IPTV services in this fledging market. and storage needed to support the video services, accounting, We present the utility of this framework by demonstrating a locality-based programming, etc. With the communications planning tool developed from this framework. infrastructure already in place and taken care by network I. I NTRODUCTION equipment suppliers and operators, IPTV service providers could still face following questions as they deploy the service: Integration of services over converged networks provides “Given a set of markets and network infrastructure, what is the opportunity for legacy players to pit against each other the initial cost of data center to enable IPTV service?” “Given by disrupting each others legacy operational territories. This the capabilities in the current deployment, what is the best can be best exemplified by the emergence of triple-play service way to accommodate new markets?” “Given the growth in the offerings. With the development of high-quality, two-way HFC market, when should the data center be expanded and the (hybrid fiber-coaxial) distribution plants, CaTV operators now communications infrastructure exploited?” provide integrated data and telephony services along with their At first glance, the problems raised by these questions seem “legacy” cable TV service. unrelated that can be approached separately. But at the core, TelCos too provide their version of high-speed Internet we find that they are variant optimization problems that can be services based on the DSLx family of communication tech- formulated using a set of common parameters with different nologies. To compete against CaTV operators’ triple-play objectives and goals. Thus, the main theme of this paper is offerings, TelCos need to add TV services to their telephone to identify this set of common parameters, and to develop a and Internet mix of services as well. To address the challenge methodology framework that can be employed when solving due to the high capacity requirements of TV offerings, the the above and other similar problems. More specifically, this major TelCos have started upgrading their networks bringing paper presents: high-capacity fiber links as close to the home as possible. With their upgraded distribution plants they too will be able • A model for IPTV service distribution and a set of key to provide triple-play services to their customers via a single parameters that can be used to analyze the performance communications conduit to their home. of an IPTV deployment. This paper focuses on the emerging deployment of TV • A general framework for planning an IPTV service de- and video-on-demand services by TelCos over their newly ployment and management based on the input parameters, upgraded networks. Using IP as the converged transport mech- and the objective of the planning (e.g. initial deployment, anism, IPTV promises a rich TV viewing experience to the future provisioning for expansion). customers by delivering digital TV programming, including • A solution design for a deployment management tool, HDTV, and DVD quality video-on-demand content. Exploiting which is developed based on the framework proposed in new compression techniques, the use of the packet-based IP this paper. protocol and multicast technologies, IPTV can utilize network IPTV is still an emerging technology and we anticipate that resources more efficiently and facilitate new service features, new knowledge will be gained as the services is deployed in such as multiple views on the same event, integrated video-on- more markets (e.g., how the viewer experience gets impacted
  2. 2. by infrastructural changes). Our framework can be extended to last mile access capture the new knowledge by adding new modules represent- tier-3 tier-3 tier-2 tier-2 tier-1 tier-1 network tier-0 tier-0 ing additional QoE parameters or refined component models. However, we project that these refinements will not neces- VSO(s) RGW/STB VHO(s) sarily modify the core structure of our overall methodology framework. 2nd mile access network (AN) Prior work in the literature has mainly focused on various SHE issues related to the development of VoD systems, broadcast and multicast technologies, server capacity planning, and site placement problems, independent of the overall issue of IPTV distribution plant distribution plant service provisioning. The novel feature of this paper is to consider them in a single framework to tackle the management issue in the domain. The remainder of this paper is organized as follows. Section network provider network provider service provider domain service provider domain domain domain client domain client domain II presents a background on the IPTV distribution model. Sec- tion III provides an abstract model for the IPTV infrastructure and presents an optimization problem. Section IV presents how Fig. 1. A three-tiered service provider plant model plus a client domain we develop various models to solve the optimization problem. Section V presents our design of a planning tool based on the models. Finally Section VI concludes the paper. access multiplexer) which performs MAC layer switching between the last and second mile networks, and (b) routers, II. T HE IPTV DISTRIBUTION MODEL if any, in the second mile network. We use dashed lines in In this section, we describe the overall plant model that the second mile access network (AN) to underscore that there represents a logical view of the typical IPTV/VoD infrastruc- may not be physical and logical point-to-point connections ture planned to be deployed by the service providers. Figure for each customer; instead the exists an aggregation of such 1 shows a typical distribution model for IPTV services. The connections that are carried in fat transmission pipes. distribution infrastructure comprises three main parts: (a) the client domain; (b) the network provider domain; and (c) the C. Service Provider Domain service provider domain. The service provider domain may The service provider domain is responsible for aggregating consist of a number of tiers; three tiers are shown in the model video content and transporting it to the edge of the network. It in Figure 1. Next, we present each of these domains in more consists of video processing sites and transport network. There detail. are three types of video processing sites in a typical setting: (a) Super Headend (SHE), (b) Video Headend Office (VHO), A. Client Domain (c) Video Switching Office. The client domain starts at the residential gateways which The Super Headend (SHE) manages and processes all are responsible for physical and MAC layer adaptation of incoming broadcast video feeds, and provides the processed the upstream link (i.e., in the direction of the provider’s video feeds to the downstream IPTV/VoD infrastructure. Typ- infrastructure) to the physical media (Cat 5, coax, twisted- ically, there will be one or two SHEs located at the core of pair cables, etc.) being used in a residence. The critical the whole IPTV/VoD infrastructure. SHE aggregates hundreds equipment in the client domain for IPTV/VoD service is the of live broadcast video signals and encodes them in various set-top box (STB) which processes commands of end-user formats (MPEG-2, MPEG-4, etc.) at multiple compression (e.g., switching a channel, or requesting an on-demand video rates and resolutions. In addition, it can insert advertisement service) and performs decoding and other processing of the in between TV programs segments. Furthermore, SHE also received IPTV/VoD feeds (e.g. recording live TV). serves as video repository, storing selected broadcast video, on-demand programming, e.g., movies and other content. SHE B. Network Provider Domain also applies appropriate digital rights management scheme to The network provider domain is responsible for distributing the video content before distributing them. various services (IPTV/VoD, VoIP, broadband internet access) A Video Head Office (VHO) typically serves a region or provided by the service provider to the client domains. A a metropolitan area. It inserts local content such as local network provider domain based on the FTTN (fiber to the TV channels and advertisements into the IPTV streams and node) architecture contains a last-mile network typically con- provides on-demand video services to the clients located in its sisting of a point-to-point technology such as some variation region. Typical equipment in the VHO facility consists of the of copper-based DSL and a second mile network typically encoders for local TV channels and inserting ads, and video consisting of a very high bandwidth technology such as a servers to stream on-demand video services. variation of gigabit Ethernet over fiber. The critical equipment A Video Switching Office (VSO) is responsible for multi- in the network provider domain consists of (a) DSLAM (DSL plexing video service with other services such as VoIP and
  3. 3. broadband Internet access. Typically a VSO would serve a viewer characteristics. In this section, we first present our neighborhood in a densely populated metropolitan area or a IPTV models, and define an optimization problem for the town, or a small city in a less densely populated region. scenario where new markets should be added to an existing IPTV network. D. Quality of experience Quality of experience (QoE) represents a collection of A. Model of the IPTV Infrastructure metrics that we use in this study to reflect the subscribers’ satisfaction from their IPTV service. The service experience In this section, we develop an abstract model of the IPTV of subscribers is a highly subjective matter and could depend network model based on the discussion in section II. Recall on very diverse parameters, from the quality of the TV units that an IPTV infrastructure consists of various sites (e.g. to the level of subscriber tolerance when talking to service VHOs, VSOs), and the network connection between these support personnel. We do not delve into these soft aspects of sites. Thus we can model an IPTV network using a graph QoE any further in this paper. Instead, we highlight below a consisting of nodes and edges, where each node denotes a site collection of quantifiable metrics that we expect to play role and the link between sites denote the interconnection between in QoE. them. In our model, each link has propagation delay (dprop ) The QoE metrics we consider are either universal or content and packet loss rate (ploss ) parameters that are constant for dependent. In the latter case, we may further distinguish the the purpose of long term planning. We also model each site QoE metrics to those that are meaningful when viewing live with different capabilities. For example, we model a VHO TV programming and to those that are meaningful to on- to host various servers (e.g., live programming acquisition demand programming. Below we summarize the collection of servers, VoD servers, fast channel switching servers, etc.) QoE metrics that we use. We elaborate further on them in with certain processing capacity to handle VoD requests and Section IV. channel zapping as well as program guide and accounting. In • Video quality: this is a universal metric, which captures this paper, we model them as queuing systems, where each the quality of the video signal arriving at a residence; server has exponential service rate and a group of servers • Channel change time: also referred to as channel zapping share a finite input buffer for queuing service requests for time, this is a live TV metric, which captures the time the simplicity of exposition. As previously mentioned, one it takes to switch a channel; this metric is important in can plug in a more sophisticate model for network and VHO IPTV because unlike in traditional CaTV service, not all facilities as they become available. channels are broadcast to a residence, but rather selected When the service provider makes a planning decision, they subsets of subsets are multicast –the latter is sometimes must know to some degree about the characteristics and the referred to as switched broadcast [2], [3]; preferences of the customers in the target market. For our • Blocking probability for VoD requests: this is an on- purpose, we just need to capture a macroscopic behavior of demand programming metric, which specifies the proba- the viewers, described, for example, by the Nielsen ratings bility that a request for video-on-demand content is not [4]. These statistics typically reveal which TV shows have satisfied. been most watched, by how many households during a certain The above QoE metrics are not necessarily exhaustive, and time period, e.g. weekly. Based on such information, we additional metrics can be supported by our framework, e.g. define a notion of community, which represents a minimal initial service set-up time, response to VCR-like commands, set of viewers that reside in a geographical proximity and etc. In any case, the service providers will consider the have similar viewing patterns. More specifically, we derive performance of their planned deployment against these metrics the channel viewing preference for each community (β) and in isolation or via an appropriate aggregation of them, e.g., if collect demographic information about the communities, and QoE metric i in the collection assumes the value qi , a service use this information in our model; the community model is planner may consider an aggregate QoE metric of the form: discussed further in section IV-A. q = h ({qi : qi ∈ Q}) , (1) B. Optimization Problem Formulation where Q represents the collection of QoE metrics considered and h represents an aggregation operator across these metrics, Now we investigate the problem of accommodating new e.g., a weighted average operator. markets given an IPTV network infrastructure as the main planning scenario. More specifically, the problem that we try III. P ROBLEM F ORMULATION to solve can be stated as follows. Assume there is an IPTV In general, we can formulate several optimization problems infrastructure currently serving a set of existing communities. for various planning and management scenarios as presented Now we are given a new set of communities with certain in the introduction. They include initial infrastructure cost viewer parameters. The problem is to find the way to maximize analysis, accommodating new markets, or future expansion the number of new subscribers without adding new resources planning. To solve these problems, we need a good model to the infrastructure. More precisely, this problem can be stated for the IPTV infrastructure, quality of experience, and the as follows:
  4. 4. reach of a community may vary and a special case of it, Problem 3.1: Community Allocation Problem Given an referred to as a neighborhood, comprises viewers that are IPTV network consisting of a set of nodes N , links L, and directly connected to a network node. In an FTTN system, communities M (of known populations), find an assignment this will be a DSLAM node, which is a switching node which between mi ∈ M and the nodes nj ∈ N such that the is capable of multicasting. In the sequel, unless otherwise total population served by the network, i Mi , is maximized specified, we will use the terms community and neighborhood while the quality of experience remains greater than a desired interchangeably. threshold Qth ; Mi represents the size of community mi . For each community j, j ∈ {1, · · · , Ncm }, we define the following parameters: In the subsequent sections, we will describe in more detail • the population Mj of the community; what we mean by assignment, quality of experience, etc. • the live TV channel viewing preference vector β j = Before delving into the details of discussion, let us examine a {βj (i), i = 1, . . . , Nch }, where βj (i) represents the few characteristics of this problem. First, we observe that at probability a viewer in community j watches channel i high level this problem can be formulated as a combinatorial and Nch represents the maximum number of channels optimization problem such as a knapsack problem or a bin offered; packing problem. Although the complexity of these problems • the VoD content duration statistics lj (x) = P r(Lj ≤ x), are NP-hard, because of their practical application efficient where Lj is the duration of watching a single VoD pro- algorithms exist for these problems that give good average gram in community j. We assume that lj is exponentially performance. Also for our application, the parameter space distributed and the duration statistics is captured by the is not large; thus even exhaustive solutions can be applied average duration: µ−1 = E{Lj }; j if the worst case performance is of concern. In our solution j j • the viewer request rate vector λj = (λT V , λV oD , design, we map the above allocation problem to a 0-1 multiple λj , . . .) of various request rates generated by the view- zap knapsack problem, which is a variant of the 0-1 knapsack ers such as events for live TV viewing, requests for VoD problem with multiple knapsacks [5]. content, and events for switching channels; additional event rates may also be included such as the calls to IV. S OLUTION D ESIGN help center. To solve problem 3.1 in section III-B, we utilize a collection The above parameters may incorporate temporal interpretation, of performance models to capture the behavior of the QoE e.g., time of day, day of a week, etc. Without lack of generality, components of interest. This collection includes models for the in our study we assume they represent averages over the peak channel zapping delay, the VoD service blocking probability, viewing hours, e.g., 8:00pm to 11:00pm. and the quality of video. We also make use of a community model to capture the collection of external parameters, e.g., the B. Channel zapping delay rate of requests for a VoD content, and the channel preference To conserve bandwidth, live TV programs in IPTV utilize of the viewers. Some of these models have been developed IP multicast rather than the traditional broadcast technologies. as part of this study, while others have been borrowed or As a result, the TV channel a viewer switches to may not extended from existing models. In either case, these models be available immediately to the viewer, because the new may be updated as we gain more understanding of certain channel may not be watched by any other viewer in the same performance components or when detailed studies researching neighborhood. Thus, in the IPTV environment, the channel their operational aspects are publicized. As mentioned earlier, switching request needs to be satisfied within the network with the addition of such updated models in the future, our rather that at TV or at the set-up box [6]. This is a major collection of performances models (which we may view as a departure from the conventional CaTV architecture where toolkit of models) will become richer, however our framework all channels are broadcast to, say, the CaTV set-up box. for the optimization methodology remains intact. We now To accelerate channel switching, or zapping, server-assisted present the models that we use for this study. solutions have been developed, such as Microsoft’s instant channel change (ICC) [1]. Although the specific mechanisms A. Community model differ from vendor to vendor, we can capture the essence of In the community model, we collect all the parameters that the network-assisted channel switching using a probabilistic capture the viewing profile of the viewers. This information is model. We can also model the load on the server using assumed to be available to service providers, either from an the server blocking model that we describe in Section IV-C. analysis of their own historical data, or from market analysis Next we will derive the average zapping delay in the IPTV from third party entities, like the Nielsen TV ratings [4]. The distribution plant. viewing profile will typically capture the aggregate behavior of We use Figure 2 to visualize the effect of the various viewers, and with this in mind, we define a viewer community community viewing profiles in the zapping delay. The figure to be a collection of viewers that reside in a geographical shows a path from community j to the data center serving proximity and can be treated as a uniform collection of viewers it with live TV content. The path includes a number of with regard to their viewing preferences. The geographical multicast-enabled nodes where TV content may be replicated
  5. 5. viewing probabilities viewing probabilities or derived from other input parameters such as the viewing at various tiers at various tiers preferences vector. For example, for our numerical results we ψ j (1; i ) ψ j (2; i ) B0 ... calculate the probability φj (0; i) of no-one in community j j θ j (1; i ) θ j (2; i ) watching channel i using the viewing probabilities and size T 1 j φ j (1; i ) φ j ( N tier − 1; i ) for the community: φ j (2; i ) B1 Mj j T j2 φj (0; i) = (1 − βj (i)) . (2) viewing profiles viewing profiles per community per community With the above definitions, we calculate the average channel B2 j zapping delay according to the following steps: j 1) Iteratively calculate φj (k; i), k = 1, · · · , Ntier − 1: ... T jN tier −1 φ j (k ; i ) probability that channel i is not watched in community set Bk φj (k; i) = φm (0; i). (3) j B N tier −1 j θ j (k ; i ) probability that channel i is not served from tier k (set T ) j k m∈Bk j ψ j (k ; i ) probability that channel i is not served from tier k for the first time tier-0 tier-1 tier-2 tier-Ntier-1 tier-Ntier The “tier” probabilities θj (k; i) and ψj (k; i) can now be calculated as: Fig. 2. A live TV path from community j to the live TV data center. θj (0; i) φj (0; i), k (4) θj (k; i) = φj (k; i)θj (k − 1; i) = φj (m; i); m=0 and branched off to serve other communities. Each node can be thought as the root of a tree whose leaves are those and communities that receive live TV content from that node. The ψj (1; i) = 1 − θj (1; i); successive nodes in the path shown are referred to as tiers, ψj (k; i) = θj (k − 1; i) − θj (k; i), with tier 0 representing the communities themselves and the j j last tier, tier Ntier representing the live TV data center. k = 2, . . . , Ntier − 1; j To calculate the average zapping delay, Rzap , we calculate Ntier −1 j j the zapping delay Rzap (j) experienced by viewers in com- ψj (Ntier ; i) = θj (Ntier − 1; i) = φj (m; i). munity j and the zapping delay Rzap (j; k) experienced by m=0 viewers in community j, who receive their live TV programs (5) from a node located at tier k. The latter means that up to 2) Calculate the “tier service” probabilities the probability the tier (k − 1) no-one was watching the channel that a k ζj that a channel change request from community j is viewer in community j switched to. We can calculate the serviced from a node in tier k parameters of interest iteratively. Next we highlight the steps Nch of calculating the average zapping delay by calculating the k ζj = i ξj ψj (k; i). (6) probability serving channels out of a tier and then use these i=1 probabilities to calculate the zapping delay. 3) Calculate the zapping delay for community j: First of all, for each tier k along the path that starts at j Ntier community j, we denote the collection of all the communities k served by that tier by T k . We also denote the collection of j Rzap (j) = ζj Rzap (j; k) all new communities that are served for the first time at tier k k=1 with Bk , i.e., Bk = T k − T k−1 . We also define the following j Ntier k j j j j probabilities: = k ζj 2dprop (m) + dproc (m) j j k=1 m=1 • φj (0; i): the probability that nobody in community j j  j  Ntier Ntier watches channel i at a random time; also, φj (k; i): the probability that nobody from the communities in the set =  2dprop (k) j + dproc (k) j m ζj , Bk watches channel i; j k=1 m=k • θj (k; i): the probability that nobody in T k , i.e., the (7) j communities served by the node at tier k, watches channel where dprop (m) j represents the propagation delay from i; tier (m − 1) to m, and dproc (m) represents processing j • ψj (k; i): the probability that channel i is found for the delay for a channel change request within the node at first time at tier k; and tier m; both of these parameters are assumed to be i • ξj : the probability that a viewer in community j upon given, however, more elaborate models for dproc (m) are j switching channels switches to channel i. possible. i The probabilities φj (0; i) and ξj may be available as part of the If a new community were to be attached to the “i-path” at community viewing profile, i.e., be external input parameters, tier k, or a new multicast-enabled node were to be introduced
  6. 6. There are data servers to support both the operational and data server administrative aspects of the IPTV service. Focusing primarily complex on the operational aspects of IPTV that we expect to be c challenged by the dynamic nature of additional load from Complex capacity: c simultaneous streams Complex capacity: c simultaneous streams Mean service time: E(S)=1/μ Mean service time: E(S)=1/μ new markets, we attempt to develop a single, mathematically when all c streams are used, when all c streams are used, c-1 up to K requests can be “buffered” up to K requests can be “buffered” tractable model that is flexible enough to capture the key a =λ/μ ... parameters of interest. For this purpose, we have adopted the m M/M/c/(c + K) queueing model as shown in Figure 3 to yes K Is any space m-1 represent the aggregation of servers supporting different data service λ available? requests services within a site, such as a VHO. This model is flexible ... no Pbl 2 enough to describe an entire VoD server complex as well as blocked a collection of servers executing a particular task, e.g., the 1 servers dedicated to enabling instant channel change. We use the multi-server parameter c of the M/M/c/(c+K) queue to capture the aggregate capability of the server collec- tion to serve the requests simultaneously. If a single server Fig. 3. The M/M/c/(c + K) model for data centers can simultaneously serve m requests and there are n identical servers, then we set c = m · n. We use the waiting room with capacity K(≥ 0) to capture the possibility that requests may between tier (k − 1) and tier k then, only the results following be allowed for queueing when all the servers are occupied. K tier k need to be updated. The portion of the calculations in represents the maximum number of requests that are allowed (3)-(7) that are applicable below tier k (i.e., from tiers 0 up to wait, i.e., if a request comes when the queue is full then to k − 1) are not affected by the new additions to the system the request will be denied service and dropped. We also use and they do not have to be redone. parameters λ for the arrival rate, and µ for the service rate. The zapping delay calculated in (7) is used in our optimiza- For a particular server complex, we use a parameter vector tion process, by checking them to be less than the maximum u = [λ, µ, c, K] to describe its characteristics. ∗ zapping delay bound Rzap (j) for each community. When the For a given parameter set u, the probability of not being service provider just needs to enforce a single zapping delay able to serve a new request is given by the blocking probability for a cluster of communities, we can compute the overall Pbl (u) [7]: zapping delay as follows. First we define νj , the probability that a channel change request originates from community j: ac a K Pbl (u) = p0 (u), where λj zap c! c νj = Ncm . (8) 1 (10) λ1 + · · · + λzap zap p0 (u) = c K . ai ac a i i! + c! c Using these probabilities, the overall zapping delay Rzap can i=0 i=1 be written as: Ncm Ncm The parameter a = λ/µ represents the traffic intensity or j=1 λj Rzap (j) zap Rzap = νj Rzap (j) = Ncm . (9) offered load to the queue. The ratio r = a/c can be viewed j=1 j=1 λj zap as a measure of the queue loading, where r < 1 implies that Finally, we would like to comment on ψj (k; i), the proba- the corresponding infinite buffer queue (K → ∞) is stable. bility that channel i is found for the first time at tier k, and its The probability p0 (u) is the probability that a newly arrived calculation in (5). We observe that ψj (k; i) is close to 1 for requests finds all servers idling, i.e., the system is empty. small k for a reasonable size community. In other words, the As c increases, the straightforward calculation of (10) channel zapping requests will not travel more than a few tiers becomes very challenging due to the presence of the factorial in most realistic scenario. For example, consider a channel that and exponential terms that result in numerical computations is relatively rarely watched, e.g., only 1% of the viewers watch involving very large numbers. As IPTV systems are designed in a relatively small community whose population is 500. and deployed to serve large number of viewers, we expect that For this channel the probability that nobody is watching this c will be in the order of hundreds if not thousands. Therefore, channel is 0.0065 within that community. Thus we expect the for the calculation of Pbl (u), we use Ham’s iterative modified channel zapping request will be satisfied without any further algorithm [7, algorithm 5.2.2]. Ham’s algorithm is designed trip with very high probability. to calculate Erlang’s B formula for M/M/c/c queues and is computationally robust for large values of c. To use Ham’s C. Data server model algorithm for Pbl (u), we have derived a simple extension of Since we need to provide diverse levels and types of it that covers M/M/c/(c + K) queues as well. According to services, data servers are abundant in an IPTV installation. the extension that we have derived, the computation of which
  7. 7. is not presented here, if: viewing experience from bad (1) to excellent (5). MPQM is a basic human vision model which takes into account the a afi−1 (a) f1 (a) = and fi (a) = (11) viewers perception of the video. Under the assumption of 1+a min {c, i} + afi−1 (a) MPEG2 encoding, [12] derives an expression that combines then Pbl (u) = fc+K (a). Compared to the Ham’s algorithm, the effects of the video coding bit rate and the network our extension introduces the term min{c, i} in the denomina- impairments on the user-perceived quality as measured based tor of fi (a) that replaces the term i in Ham. This allows for on the MPQM model: the iteration (11) to proceed until i reaches the value (c + K), R − ξ1 not just to c as is the case with Erlang’s B formula. We note Q = Q0 + χQ × ( ) R + χL × R × P LR, (12) here that an interesting property of the M/M/c/c queue (i.e., χR when K = 0) is that the blocking probability coincides to that where R represents the encoding rate of the MPEG2 encoder; of the M/G/c/c queue as well [8]. Hence, in this case, the P LR represents the packer loss ratio; Q0 represents the assumption on exponentially distributed service times can be “reference” rating for excellence (i.e., 5). However, Q0 as well relaxed. as the remaining parameters, which are related to the spatio- In closing, we recognize that one may choose to use a temporal complexity of the video, are derived through curve more elaborate models for, say, the VoD server infrastructure, fitting techniques that try to match (12) against measured data to better describe their scenario. There is a vast literature collected during video playback. accumulated on this topic from the past when VoD system design was a hot research topic, e.g., [9]-[10]. We note V. D ESIGN OF THE P LANNING T OOL that the majority of these models are either straightforward A. Software Architecture applications of Markovian queueing models (similar to our In this section, we describe the software architecture of M/M/c/(c+K) model) or elaborate extensions of these mod- an IPTV planning tool that we have developed as a proof els, which typically represent instantiations of specific VoD of concept of the proposed framework. The front-end of the designs, which may or may not be representative of the VoD software tool is an interface to take various input data such as system adopted by the service provider. While we can certainly various given parameters (e.g. network configuration, server consider these more elaborate models in the framework, doing characteristics, viewer profile) and management objectives so will unnecessarily distract us from the main thesis of this (e.g. video quality threshold, VOD service rate). We call them study: to capture the aggregate performance behavior for the independent parameters. These independent parameters are purpose of planning. As discussed in III-B, this is already a processed and converted into a set of derived parameters that challenging task in itself, and the adoption of an elaborate are ready to be consumed by the main algorithm. An example VoD server model can be always considered if analysis at of such derived parameters is φj (0; i) – the probability that such a detailed level is desired by the service planner, which nobody watches channel j, which have been derived in our may need to be assumed being very knowledgeable of the example from the community viewing preferences βj (i) (see technical details of the data servers used for providing the (2)). The tool may include several algorithms that are applica- IPTV services. ble for various planning tasks. For the problem we consider in this paper, we use a knapsack algorithm to find the best D. Video quality models assignment of the communities to the IPTV nodes. Figure 4 Viewing video content is a subjective matter and so is the presents a functional diagram of this planning tool. perceived quality of the video viewed. Quantifying a video quality is an important topic, but it is outside the main theme B. Algorithmic Structure of our study. Instead, we adopt video performance models In Section III-B, we have mentioned that the community developed by others to describe the video quality as a function allocation problem can be formulated as a knapsack problem. of measurable quantities. These models have been derived We describe how we can use a knapsack algorithm to solve through a combination of empirical and analytical means. As our problem. Recall the 0-1 multiple knapsack problem (MKP) with other models that we have presented in this paper, the for n given items and m given knapsacks with capacity ci , i = video quality models could be substituted by alternative ones 1, . . . , m: if necessary within our framework. In the IPTV environment, video quality is mainly affected m n maximize i=1 j=1 vj xij by network and content specific factors. The main network n subject to j=1 wj xij ≤ ci , i = 1, . . . , m, dependent parameters that affect the video quality are delay, m jitter and packet loss. The application specific parameters are i=1 xij ≤ 1, j = 1, . . . , n, xi j ∈ {0, 1}, i = 1, . . . , m, j = 1, . . . , n, the video codec, the encoding bit rate, packetization scheme, the loss recovery technique, and the characteristics of the video where vj is the profit for including item j in the knapsack, xij content. is an indicator variable whose value is 1 if item j is included In this paper, we adopt the moving pictures quality metric in the knapsack i, and 0 otherwise, and wj is the size of item (MPQM) [11], which represents a numeric score denoting a j.
  8. 8. • Server capacity: This is an easy parameter to handle since a server typically has a fixed bound for the rate of request independent (observable it can handle or the number of simultaneous streams that & measurable) parameters it can send out at any time. Thus we can treat it like the (inputs) weight of the item in the MKP. • Channel zapping delay: In the allocation problem, we just parameter Parameter IPTV deployment transformations need to consider this parameter for the new community, transformations planner (human-in-the loop) planning tool and calculations - selects input parameters derived - selects optimization objectives since adding a community does not increase the probabil- parameters - runs what-if scenarios ity θj (k; i) – the probability that nobody in Tjk watches - develops deployment strategies optimization algorithm(s) channel i – of already existing communities for all k. Service deployment candidates Using the iterative calculation in (5), we can efficiently (outputs) test this condition. • Service blocking probability: This parameter can be easily tested for each sites using (11) because it depends on the site parameters λ, µ, c, K – and among these parameters, only λ changes when changing the community allocation. Fig. 4. A diagram of the planning tool for IPTV service management Under the Poisson assumption, we can simply update it by λnew ← λold + λj for the new community j. • Network parameters: For this parameter also, we just need to consider the new community since the funda- Although the knapsack problems are NP-hard in general, mental network characteristics do not change for existing due to the wide applicability of the problem there are myriads communities. of efficient heuristics, which provide good performance in It is worth noting that the overhead for computing these most practical cases. For example, [5] presented an efficient parameters is very small. In particular, the complexity for algorithm for MKP that can solve large instances with n = checking the server capacity and the network parameters is 100, 000 in a fraction of a second. Thus in this paper, we focus constant, and the complexity for testing the channel zapping on how we can translate our allocation problem to a knapsack delay and the server blocking probability involves iterative problem. steps that are dependent on the number of tiers and the number At high level, this relationship is not very difficult to see: of server clusters. Therefore, it is possible for us to solve the each community is an item; each IPTV node is a knapsack with entire community allocation problem efficiently. certain capacity; and connecting a new community has some value to the service provider. But obviously we cannot directly C. Case Study - Adding New Markets apply the knapsack algorithm to our formulation because Now consider a concrete planning scenario that a service the community parameters (e.g. viewing profile, request rate) provider has two VHOs near mid-size cities that are currently cannot be directly compared with the resources in an IPTV over-provisioned. The service provider would like to take network (e.g. server capacity). Thus we need to use our models advantage of the over-provisioned VHOs and try to serve presented in Section IV to calculate the impact of adding a new ten new emerging communities out of these two VHOs. It community to a VHO. is possible that the over-provisioned VHOs are not sufficient Depending on the flavor of a particular optimization prob- to serve all ten new communities. lem, we may need to impose various different constraints such Each VHO has several routers connected to it. These as the quality of experience (QoE) of the viewers, the limit on routers are multicast-enabled nodes and provide access to the financial budget, or the market parameters. In the allocation households in the communities. Initially there are 8 com- problem considered in this paper, the constraint is on the QoE munities already connected to the VHOs (see Figure 9.a). perceived by the users that it should be greater than some Each community has been assigned to a VHO based on threshold Qth . The user QoE is affected by several factors. the capacity of the site and geographic proximity. However, First, the live TV quality is affected by the stream processing the connection to the routers is solely determined by the capacity of servers because they should be able to handle geographic location because the routers only perform simple the aggregate of request from the users. The user experience multiplexing functions. In this setting, we now try to add 10 will be also affected by the channel zapping delay. Second, new communities, and find the best allocation in terms of the for the VOD service, the main parameter of concern is the number of household served by the existing infrastructure. service blocking probability for starting a new movie and for Figure 5 presents the parameters that characterizes the com- handling VCR-like commands. Finally, for both the services, munities – population, peak VoD request rate, and aggregate network parameters, such as packet loss rate and jitter, play live TV channel change rate. We tried out many sets of an important role in the overall QoE. Now we examine how parameters and we show one such instance to illustrate the these parameters can be handled when we apply the problem operation of the tool. Figure 6 presents the network parameters to a MKP: relevant to each community, namely the last mile packet loss
  9. 9. Existing Communities New Communities Community Id j 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Mj (K households) 10 25 450 80 650 400 150 250 50 90 75 350 75 850 90 40 800 20 λj (reqs/hr) vod 330 550 730 720 530 680 720 210 50 950 85 390 680 770 560 200 590 100 λj (inst/sec) zap 50 150 450 90 500 490 200 390 90 130 110 490 100 650 400 390 800 190 Fig. 5. Community Profiles for Existing and New Communities Existing Communities New Communities Community Id j 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Last mile PLR (%) 0.02 0.01 0.03 0.02 0.04 0.02 0.06 0.01 0.05 0.10 0.07 0.05 0.06 0.04 0.02 0.05 0.02 0.04 Last mile distance (km) 1.6 1.2 1.8 1.5 3.2 1.5 4.8 8.0 4.0 8.0 5.6 2.0 4.5 3.5 1.5 4.0 1.5 3.5 Fig. 6. Network Parameters for Existing and New Communities VHO 1 VHO 2 Server type A-server V-server A-server V-Server Max. Number of Streams 100 400 150 600 Processing Delay (sec) 0.004 0.003 0.005 0.006 Fig. 7. Server Parameters for VHOs Zapping delay Video quality VOD blocking prob. (sec) (MPQM) VHO 1 0.890 (< 1.0) 4.72 (> 4.6) 4.237e-17 (< 1.0e-5) VHO 2 0.958 (< 1.0) 4.68 (> 4.6) 3.396e-87 (< 1.0e-5) Fig. 8. QoE parameters with the existing allocation (a) (b) connected to the VHO that the community has been assigned to. Using our proof of concept planning tool, calculating this SHE SHE allocation took less than 25 sec from start to finish. We believe an optimized implementation could easily speed up V1 V2 V1 C14 V2 the process and perform the same type of allocation task in C11 several seconds. Thus, we anticipate the planning tool can be R5 R5 C4 C4 R1 R1 C9 used in an interactive manner. C8 C8 C16 C1 R2 R3 C1 R2 C12 R3 VI. C ONCLUDING R EMARKS C5 R4 C17 C5 R4 Convergence of residential services (voice, telephony, and C2 C3 C2 C3 C6 C7 C18 C6 C7 Internet access) over IP opens up new opportunities that blurs C15 SHE super head-end the distinction between communication services providers. The C10 V video home-office (VHO) use of a single conduit and transport substrate to bring inter- C13 R router (a multicast-enabled node) active broadband and narrowband data services to residential C community C new community customers simplifies the overall network management and service deployment. Furthermore, this facilitates the creation of a new breed of integrated applications that are able to Fig. 9. Community allocation before and after adding new communities exploit a diverse set of residential services such as video-phone on a TV. TelCos are currently working feverishly to upgrade their rate and the last mile distance. The distance numbers were networks to enable converged data services by bringing fiber as obtained by mapping each community to a city in the Westch- close to residences as possible. Over these upgraded networks, ester county of New York, and the packet loss rates have been they plan to add IPTV services to complement and enhance derived from the distances. Figure 7 presents the aggregate their existing voice and Internet access services. However, the stream processing capacities and the mean processing delays combination of new network infrastructure and vast data cen- of the servers in each VHO. All of the parameters in Figure ters to provide IPTV services to a large number of customers 5 – Figure 7 are input parameters to the tool. requires a significant upfront capital investment. This in turn Figure 9.b presents the resulting allocation. We observe that means that careful planning and deployment of services that all but two communities (10 and 13) could be assigned without make best use of available resources to maximize return-on- violating the QoE constraints of both the new and existing investment while maintaining a desired quality of experience communities. Figure 10 presents the resulting QoE values becomes very important. after the community assignment. For the new communities With the above in mind, in this paper, we have focused on also, each communities are assigned to the nearest routers a framework to aid planning and managing the deployment
  10. 10. Zapping delay Video quality VOD blocking prob. (sec) (MPQM) VHO 1 0.971 (< 1.0) 4.67 (> 4.6) 9.011e-6 (< 1.0e-5) VHO 2 0.958 (< 1.0) 4.68 (> 4.6) 1.682e-9 (< 1.0e-5) Fig. 10. QoE parameters after assigning the new communities of IPTV services. To the best of our knowledge, this is the [4] “Nielsen media research,” first such effort in this area. The framework brings together a /Public/. [5] D. Pisinger, “Algorithms for knapsack problems,” Ph.D. dissertation, collection of models that capture the aggregate macroscopic University of Copenhagen, February 1995. behavior of representative aspects of an IPTV service that [6] C. J. Cho, I. Han, Y. Jun, and H. Lee, “Improvements of channel zapping impact service performance and hence the quality of experi- time in IPTV services using the adjacent groups join-leave method,” in Proc. 6th Int’l Conf. on Advanced Commun. Technology, Phoenix Park, ence from the service, like the viewer profiles, zapping delay, Korea, Feb. 9-11 2004, pp. 971–975. data server blocking probabilities, etc. Some of these models [7] H. O. Allen, Probability, Statistics, and Queueing Theory with Computer were derived specifically for this framework, while others were Science Applications, 2nd ed. Academic Press, 1990. [8] F. Kelly, “Loss networks,” Annals of Applied Probability, vol. 1, pp. adopted from previous studies, not related to IPTV. 319–378, 1991. These models are used to map a set of external (indepen- [9] V. O. K. Li, W. Liao, X. Qiu, and E. W. M. Wong, “Performane model dent) parameters, which are assumed to be available to the of interactive video-on-demand systems,” IEEE JSAC, pp. 1099–1109, August 1996. service deployment planner, to a collection of performance [10] S.-H. G. Chan and F. A. Tobagi, “Modeling and dimensioning hier- metrics that are reflective of a particular deployment configu- archical storage systems for low-delay video services,” IEEE T. on ration. A deployment configuration comprises a specific set of Computers, pp. 907–919, July 2003. [11] C. J. van den Branden Lambrecht and O. Verscheure, “Perceptual quality (a) service support resources (e.g., data servers); (b) network measure using a spatio-temporal model of the human visual system,” in nodes and topology; (c) communities of viewers served by Proc. of SPIE, vol. 2668, San Jose, CA, Jan. 28-Feb. 2 1996, pp. 450– those resources through the specific network; and (d) network 461. [12] O. Verscheure, P. Frossard, and M. Hamdi, “MPEG-2 video services nodes at which the communities attach to the network. The over packet networks: Joint effect of encoding rate and data loss proposed methodology then scans through the collection of on user-oriented QoS,” in Proc. 8th Int’l Workshop on Network and possible deployment configurations to select the one that best Operating Systems Support for Digital Audio and Video (NOSSDAV’98), Cambridge, UK, July 8-10 1998, pp. 257–264. satisfies the deployment objectives. Depending on the complexity of the deployment options either straightforward exhaustive scans, or intelligent scans, like the ones considered in this paper, can be used. Since queueing system representations of resources are an integral part of the framework, intelligent configuration scans can also take advantage of straightforward necessary conditions for the λ stability of these queues (e.g. load µ < 1) to exclude a set of configurations from the scans that will be guaranteed to violate the quality of experience objectives, without having to calculate the corresponding performance metrics. Finally, different deployment objectives can be studied through the framework, e.g., deploying the service to new communities based on currently available resources, based on newly acquired resources, based on a given collection of qual- ity of experience thresholds, etc. The framework also allows the study of deployment strategies that consider how staggered deployments to new communities will fair against anticipated resource enhancements, e.g., installation of additional servers, or study trade-offs in attaching new communities to different nodes in the distribution network. R EFERENCES [1] Microsoft, “Microsoft TV IPTV edition,” /IPTVEdition.mspx. [2] N. Sinha and R. Oz, “The statistics of switched broadcast,” in SCTE 2005 Conference on Emerging Technologies, January 11-13 2005. [3] Z. Avramova, D. D. Vleeschauwer, S. Wittevrongel, and H. Bruneel, “Models to estimate the unicast and multicast resource demand for a bouquet of IP-transported TV channels,” in Proc. 2006 Int’l Workshop “Towards the QoS Internet” To-QoS’2006, Univ. of Coimbra, Coimbra, Portugal, May 19 2006.