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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publicat...
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publicat...
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publicat...
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publicat...
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publicat...
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publicat...
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Utility-Based Resource Allocation for Layer-Encoded IPTV ...

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Utility-Based Resource Allocation for Layer-Encoded IPTV ...

  1. 1. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings. Utility-Based Resource Allocation for Layer-Encoded IPTV Multicast in IEEE 802.16 (WiMAX) Wireless Networks Wen-Hsing Kuo, Tehuang Liu, and Wanjiun Liao Department of Electrical Engineering National Taiwan University Taipei, Taiwan Email: wjliao@ntu.edu.tw Abstract-In this paper, we propose a utility-based resource environment in which all users may have different channel allocation scheme for layer-encoded IPTV multicast conditions, this layering approach can provide each user a streaming service over IEEE 802.16 WiMAX networks. reasonable amount of resource depending on its channel Unlike existing utility-based schemes, this mechanism is designed for wireless networks which support adaptive quality and the popularity of each program, thus achieving modulation and coding. Each video stream (or program) is good utilization of the total system. encoded into different layers. Then, our mechanism adjusts the number of each user’s received layers dynamically according to its channel condition and the available network bandwidth, so as to maximize total utility. We prove that this problem is NP-hard, and show that our scheme is bounded in performance to the optimal solution and can run in polynomial time. The simulation results show that this scheme can allocate resource flexibly according to the utility function of each program, the popularity of each program (i.e., the number of users receiving each program), and the amount of total available resource in the network. The result also shows that the fairness of the system can be guaranteed. Index Terms-IEEE 802.16, IPTV multicast, layer encoding Figure 1. Layer-encoded multicast in a WiMAX network. I. INTRODUCTION IEEE 802.16 (WiMAX) is a promising last mile Very few existing research is related to IEEE 802.16’s technology for broadband wireless access. The major network multicast MAC schemes. In [1], a reliable multicast scheme is components in such a network include a Base Station (BS) and proposed by using Code Division Multiple Access (CDMA) multiple Subscriber Stations (SS), as shown in Fig. 1. The codes in WiMAX networks. In [2], a multiplex-multicast downlink direction (i.e., from the BS to SSs) is a broadcast scheme is designed to resolve the transmission bottleneck channel and the uplink direction (i.e., from SSs to the BS) is a between IEEE 802.16 and IEEE 802.11. There are also very multiple access channel shared by all SSs. Compared to IEEE few existing papers which have applied layer encoded 802.11 (WiFi), WiMAX has a higher bandwidth and broader technology to WiMAX multicast environments. The work in coverage range. This makes WiMAX an excellent platform to [3] proposes a utility-based multicast scheme for provide IPTV streaming service to residential users. layer-encoded multicast sessions. That work, however, assumes that all subscribers have the same channel condition To provide high-quality IPTV multicast service, radio and thus receive the same data rate given the same amount of resource allocation is the key. In this paper, we propose a bandwidth. Consequently, it is only suited for wired networks. utility-based resource allocation scheme, for layer-encoded Another paper [4] takes adaptive modulation and coding into multicast streams, which adapts to dynamic channel condition, consideration for layer-encoded streams. The system allocates and can be integrated into the multicast mechanism in WiMAX a different modulation scheme to each layer and adjusts each standards. layer’s allocated resource to maximize the total utility. The In our system, each video stream (or program) is encoded utility function used in that work is defined as the user into several sub-streams (i.e., layers). The first layer is called satisfaction given that its subscribed layers are served by a the base layer, and the others are called the enhancement layers. certain amount of resource. However, it fixes the number of The more layers a subscriber has received, the better the video layers each user can receive once it has joined the service. As quality. For each layer-encoded stream multicast through the a result, wireless BSs can only adjust transmission bandwidth network, different receivers may receive different numbers of among layers of each program. In reality, especially in an layers according to their channel quality. In a wireless environment like WiMAX which can provide video services, 1-4244-0353-7/07/$25.00 ©2007 IEEE
  2. 2. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings. each user usually subscribes to streams only and let the system Since layer-encoded programs are supported in the determine the allocated resource and video quality. That work, scheme, we let each layer be a multicast sub-session, and therefore, cannot use radio resource efficiently. mark each layer by a unique vector s=[m,l], where m is the program, l is the lth layer of program m. For all layers in a In this paper, we look at the problem in a different way program, the combination of the multicast subscribers and from all existing proposed schemes. To increase the flexibility their channel conditions are identical. In addition, since of the system and utilize the radio resource more efficiently, layer-encoded programs can only be decoded in order, the the number of subscribed layers for each user is adjustable. higher layer will not be assigned to users unless lower layers This assumption reflects reality more accurately and the have already been assigned. Fig 2 shows the relationship requirements made by WiMAX. We then propose a between programs (1-M), program m’s layers ( 1 − Lm ) and utility-based resource allocation scheme for IPTV multicast. subscribers ( 1 − N m ) . Since the stream rate of each layer The characteristics of this scheme are summarized as follows. 1) It supports layer-encoded streams and determines the number differs, we let R s denote the data rate of layer s. Denote by of layers to be received by each user dynamically. 2) It can be R m (n) the rate of a burst profile with which the first n users executed periodically or on-demand to reflect user’s in group m can receive the program. This corresponds to the time-variant channel condition. 3) It can be applied to WiMAX burst profile with the minimal rate among the first n users in and all other networks in which users’ channel conditions can  T ⋅ Rs  be monitored and the transmission rates can be adjusted the group. Thus, Ts (n) =   is the number of accordingly. 4) It can improve the total user satisfaction and  R m (n)  the system channel utilization. 5) It can support single layer timeslots required to transmit this layer to n users. media streams, and can be applied to unicast media streams by treating each unicast stream as a multicast stream with only one subscriber. In other words, it supports unicast/multicast, single-layer/multi-layer environment and is very flexible. The performance of our scheme is evaluated by simulation and analysis. The complexity of this scheme is also discussed. The rest of the paper is organized as follows. In Sec. II, the system model is described and the utility-based multicast scheme is proposed. In Sec. III, the details of the algorithm are presented. Its performance and complexity are also analyzed. Figure 2. The relationship between program m and its supported In Sec. IV, the simulation results are provided. Finally, the number of layers and subscribers. paper is concluded in Sec. V. II. Utility-Based Layer-Encoded Multicast Scheme In this work, the utility value of each layer is defined as A. System Design a user’s additional satisfaction when this layer is received (i.e., the utility function is a stair-like function). Denote the utility The burst profile of WiMAX is a set of codec settings values of the Lm layers in program m by (which reflects the channel condition) for BS to transmit data U m = [U [ m,1] , U [m, 2] ,...., U [m, Lm ] ] . The utility a user obtains is the to SSs, and each SS needs to negotiate its burst profile with the BS before the connection starts. Different burst profiles result summation of the utility values from all its received layers, n in different levels of robustness and transmission rates. The more robust the burst profile is, the lower the data rate is. i.e., ∑U k =1 [ m ,k ] , where n is the number of layers the user When multicasting a stream to a set of users with different receives. Fig. 3 depicts program m’s utility value of each burst profiles, the BS must choose the most robust burst profile layer. (i.e., the one with the lowest data rate) so that all users can receive the stream with the same (worst) video quality. U[m,Lm] In our system, given a user group, the BS picks up group members one by one into service in decreasing order of their U[m,2] channel conditions and use the most robust burst profile among all selected users to transmit data. This is because to multicast a …… stream to a set of subscribers, serving those with better channel U[m,1] quality first leads to better resource utilization. To multicast a program to more users, a more robust burst profile must be Layer 1 Layer 2 Layer Lm used, and hence it takes more time slots to transmit the program. Figure 3. The program m’s utility value of each layer.
  3. 3. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings. The design of a utility function is an open question. A Us utility value of a video layer can be the video quality perceived marginal utility of layer s is ∆U = which denotes the ∆Ts by the receiver. We could use some metrics, such as the average utility of ∆Ts (n) time slots. perceived signal-to-noise ratio (PSNR) [5] and the mean-opinion-score (MOS), to construct the utility function. In the for loop of our U-LEM algorithm, the system However, we leave this issue to other researchers and focus on picks up all compulsory layers and serves them first. Then, in optimizing the total utility. the do loop, for each layer, it finds an unserved user who has The goal of our work is to adjust the allocated resource the best channel quality and is likely to be served. (i.e., its between layers and programs in order to maximize the total lower layers have all been served and its required resource is smaller than the system’s available resource, i.e., utility ∑U s s Ns , subject to ∑ T ( N ) < T , where s s s Ts ( N s ) ∆Ts ( N s ) < Tc ). S max includes the candidate user which has the highest ∆U among all layers. This loop repeats until all is the total slots allocated to layer s (note that s=[m,l]), and T is available resource Tc is consumed or no more users can be the total available slots. served. Depending on the requirements of the subscribers and the multicasting policy implemented by the operators, certain U-LEM() layers (e.g., all programs’ base layers) can be made compulsory and all of its subscribers must be served. If this is the case, the Tc ← T ; system must serve compulsory layers first and then allocate residual resource to the other layers according to our approach. for all unserved compulsory layers s, NS = Nm ; Tc ← Tc − Ts ( N m ) ; B. Notations end for; The notations used in this paper are listed as follows. M: number of programs provided in the WiMAX network do N m : number of subscribers for program m S max ← null ; Lm : number of layers for program m Us s=[m,l]: program m’s layer l S max = arg max s's lower layers are served, ∆Ts ( N s )<Tc {∆U s ( N s ) = }; ∆Ts Rs : streaming data rate of layer s If ( S max ≠ null ) U s : the additional utility value when a user receives layer s Tc ← Tc − ∆TS max ; Rm (n) : data rate of the burst profile able to serve N S max = N S max + 1 ; n subscribers in program m  T ⋅ Rs  end if; Ts (n) =   : number of time slots required to transmit Loop until ( S max is null or all users are served).  R m ( n)  layer s to n subscribers. Figure 4 Utility-based layer encoded multicasting scheme. T: network total available timeslot in a service period N s : number of users actually served by layer s III. DISCUSSION ∆Ts (n) = Ts (n + 1) − Ts (n) : the time slots required to add one A. Performance user in layer s. In this section, we prove the problem is NP-hard and our proposed algorithm is a sub-optimal solution with some given Us ∆U s (n) = : marginal utility of Ts (n) assumptions. ∆Ts (n) Lemma 1 The maximization problem is NP-hard. Tc : residual time in a service period Proof: S max : the layer that has the largest marginal utility We first consider an NP-hard problem called knapsack 0-1. In knapsack 0-1, the total value of boxes put into the bag, C. Proposed Scheme We propose a resource allocation algorithm called i.e., ∑Vi i , is maximized, subject to the total weight of the Utility-based Layer-Encoded Multicast (U-LEM), to solve the boxes not exceeding the limitation of the bag (denoted by W), problem, as shown in Fig. 4. We first define the marginal utility ∆Ts (n) as the time slots required to serve one additional i.e., ∑W ≤ W .i user given that n users have already been served. Thus, the i
  4. 4. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings. This utility maximization problem can be reduced from a This algorithm can be run in polynomial time. Denote the maximal number of layers and subscribers among all conventional knapsack 0-1 problem because any box, say, programs by Lmax and N max , respectively. The complexity ( Vi , Wi ), can be transformed to a user with ( U s , ∆Ts (n) ) by of the first term in the for loop is letting s=[i,1] and n=0. Therefore, any instance (i.e., set of M Lm boxes) in knapsack 0-1 can be mapped to an instance of our problem (i.e., set of users). Since knapsack 0-1 is NP-hard, this O( ∑L s s∈compulsorylayers )= O ( ∑∑ N ) = O(ML m =1 l =1 s max N max ). problem is also NP-hard. For the do loop, since this algorithm scans all layers to Lemma 2 The performance of the proposed scheme is bounded find S max and serves one user at a time, the complexity of to the optimal with the following assumptions. Lm M 1. Layers of each program are sorted in decreasing order each step is O ( ∑∑ N ) = O(ML s max N max ) , and it is of their utility values, i.e., U [ m,l ] ≥ U [ m,l +1] for all m m =1 l =1 and l. repeated at most O (MLmax N max ) times. Thus, the complexity is O ((MLmax N max ) 2 ) . 2. For all s, the number of time slots required to serve one user deceases as n increases, i.e., Therefore, the complexity of the algorithm is: ∆Ts (n) ≥ ∆Ts (n + 1) . O (MLmax N max ) + O ((MLmax N max ) 2 ) = O ((MLmax N max ) 2 ) , Proof: which is polynomial time. We prove this by relaxing the following two constraints. The first constraint is that the user should be served at ∆Ts (n) IV. SIMULATION RESULTS timeslots to acquire U s . We relax it in such as way that for all A. Simulation Parameters n and s, resource ∆Ts (n) can be served partially to get a partial U s . The second constraint states that a layer can be We conduct simulations to evaluate the performance of allocated only when lower layers have already been served. our scheme. In the simulation, the data rate of each user’s burst This constraint is relaxed such that the system can serve any profile Rm (n) is uniformly distributed over [0.1, 0.2, ,0.9, 1] . user in any layer at any time to acquire U s . We simulate two four-layer programs. Their respective utility To describe the optimal solution of the relaxed problem, functions are given in Fig. 5. The rate (i.e., Rs ) of each layer is we add one user into the layer with the largest ∆U s , i.e., S′ max set to 1. at each step until all resources are consumed. Given the two assumptions above, ∆U s ( N s ) decreases as both n and l Utility functions of program 1 and program 2 1 1 increase. Therefore, S max is equal to S′ in each step of the max 1 0.95 do loop except the last user who is partially served in the 0.9 0.85 0.8 0.75 relaxed problem. Thus, the performance difference between 0.7 these two algorithms is given by ∆U MAX = max{∆U s } , i.e., 0.6 0.6 s 0.5 Utility program 1 0.5 the performance difference between our algorithm and the program 2 0.4 optimal algorithm (which cannot outperform the optimal 0.3 0.25 solution of the relaxed problem) is not larger than ∆U MAX . 0.2 0.1 The assumptions mentioned above match normal network 0 conditions. Normally, in a layer-encoded stream, the higher layer 1 layer 2 layer 3 layer 4 layers must have a lower utility value; otherwise, it would have been selected as a lower layer to provide better performance. In Figure 5. The utility functions used in the simulations addition, as n increases, corresponding to poorer user channel B. Simulation I quality, it takes more resource (i.e., time-slots) to serve one more user. However, our scheme can also be proved to be In the first simulation, we compare the behavior between bounded without these two assumptions [6]. Due to space a layer-encoded program and a single layer program. The limitations, we will not include the proof in this paper. system multicasts a program 2 and a single layer program to 50 users. The data rate of the single-layer program (i.e., Rs ) is 4, B Complexity and its total utility is 1. Since the utility and data rate of this
  5. 5. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings. single layer program are equal to the sums of the corresponding who receive at least one layer of programs) among all values in the four layers of program 2, we can see the subscribers. We find that when there are only 10 users significance of the layering approach. The total resource subscribing to program 1, there are still about 45% of program allocated in each case varies from 0 to 50. The result for each 1 users (i.e., 4.5 users on average) being served. This fact point in the two curves shown in Fig. 6 is the average over 1000 shows that even when only a small number of subscribers are runs. receiving a certain type of program, they will not be starved. On the other hand, the proportion of allocated users for We observe that given the same amount of total resource, program 2 is higher than that for program 1 under the same layer encoded programs always achieve a higher total utility, condition. This is because the first layer of program 2 has a implying that it has better resource utilization. This is because higher marginal utility and the system tends to serve users with layer-encoded programs can provide more options to the system the highest marginal utility first. and allow more flexibility in allocating resource, leading to a higher total utility. Such effect is even more significant when From this simulation, we show that even when the radio resource is scarce. number of subscribers is small, starvation of a certain program will not happen. Thus, fairness among different programs is Total utility values of the 4-layer program and the guaranteed. With the usage of compulsory layers, fairness can single-layer program be further enhanced. 50 45 40 Numbers of subscribers of program 1 and program 2 35 Total Utility 30 100 Single Layer 90 25 Program 20 Program 2 80 [1,1] 70 [1,2] 15 Number of User [1,3] 10 60 [1,4] 5 50 [2,1] 40 0 [2,2] 0 5 10 15 20 25 30 35 40 45 50 30 [2,3] 20 [2,4] Total Resource 10 0 0 10 20 30 40 50 60 70 80 90 100 Figure 6. Comparison of total utility for layer-encoded and single layer % of Program 1 User approaches. Figure 7. Popularity of each program. C. Simulation II Percentages of programs 1 and 2 In the second simulation, the total number of users is fixed at 100, and the total resource is fixed at 300. We tune the ratio 100% between program-1’s and program-2’s subscribers to observe the 90% Percentage of Served User 80% behavior of the system. Again, each result is the average over 70% 1000 runs. 60% Program 1 50% Program 2 40% Fig. 7 shows the number of users in each layer of programs 30% 1 and 2. The item “[x,y]” in the graph denotes the number of 20% users receiving program x with y layers. We observe that when 10% 0% the number of subscribers increases (i.e., become more popular), 0 10 20 30 40 50 60 70 80 90 100 the system tends to allocate more resource to that program in Number of Program 1 User order to satisfy more users’ needs and increase the total utility. In addition, different utility function results in a different pattern. Figure 8. The percentage of users served in each program. Since the marginal utility of each layer for program 1 is identical, the number of allocated users in each layer is almost identical. V. CONCLUSION AND FUTURE WORK On the other hand, the utility function of program 2 is concave, leading to more users allocated to lower layers than to higher In this paper, we have proposed a utility-based resource layers. This result shows that the system can allocate the amount allocation scheme, called U-LEM, for layer-encoded multicast of allocated resource according to the popularity and the utility streaming service in WiMAX networks. This scheme can run function of the program. in polynomial time and is bounded to the optimal utility. The performance of the proposed scheme is evaluated by Fig. 8 shows the percentage of allocated users (i.e., those
  6. 6. This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2007 proceedings. simulations. We show that under the same conditions, layer-encoded programs have higher total utility than single-layer programs. Thus, layer encoding is suggested in WiMAX environment. We also show that the system can tune the allocated resource to users according to the number of subscribers and the utility function of each program. However, even when the difference in the number of subscribers is very large, none of the programs gets starved and thus fairness is maintained. In the future, we will integrate this multicast resource allocation scheme with a scheduling algorithm and call admission control scheme to provide an integral BS solution for WiMAX wireless networks. ACKNOWLEDGEMENT This work was supported in part by National Science Council (NSC), Taiwan, under a Center Excellence Grant NSC95-2752-E-002-006-PAE, and in part by NSC under Grant Number NSC95-2221-E-002-066 REFERENCES [1] H. Lee and D.-H. Cho, “Reliable multicast services using CDMA codes in IEEE 802.16 OFDMA system,” in Proc. IEEE VTC 2005-Spring, 2005. [2] P. C. Ng, S. C. Liew, and C. Lin, “Voice over wireless LAN via IIEE 802.16 wireless MAN and IEEE 802.11 wireless distribution system,” in Proc. International Conference on Wireless Networks, Communications and Mobile Computing, 2005. [3] Wing-Fai Poon, Kwok-Tung Lo and Jian Feng, "Performance study for streaming layered encoded videos in broadcast environment," Proc. ICITA 2005. [4] J. Kim, J. Cho, and H. Shin, “Resource allocation for scalable video multicast in wireless cellular networks,” in Proc. WiMob 2005. [5] J.-R. Ohm, “Description of Core Experiments in MPEG-4 Video,” ISO/IEC JTCI/SC29/WG11, N2554, 1998. [6] Wen-Hsing Kuo, Tehuang Liu, and Wanjiun Liao, “Utility-based Radio Resource Allocation for IPTV Multicast Service over IEEE 802.16 (WiMAX) Wireless Networks,” NTU Technical Report NTU06-2006-129, Aug. 2006.

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