COOPERATIVE MULTI-CELL BEAMFORMING FOR MIMO UNICAST/ MULTICASTBROADBAND H.264 SCALABLE VIDEO NETWORKS

Naveen K. D. Venkategowda , Nitin Tandon, Aditya K. Jagannatham

Department of Electrical Engineering, Indian Institute of Technology Kanpur, India 208016Email: {naveendv,ntandon,adityaj}@iitk.ac.in

ABSTRACT

In this paper we propose a novel scheme to jointly deter-mine the optimal transmit/receive beamformers together withmulti-user power allocation towards transmission rate max-imization in a cooperative MIMO cellular wireless networkfor unicast/multicast scenarios. For the unicast scenario, wepropose a successive constrained eigenbeamforming (SCEB)technique to reduce the inter-user interference and enhancethe data rates subject to power constraints. This scheme is fur-ther extended to a multicast scenario (SCEB-M) to maximizethe sum rate of a user group with group constraints on thetransmit power. We also derive optimal schemes to efficientlyschedule a subset of users/ groups from the active user set.We employ a practical H.264 scalable video quality modelto demonstrate the performance of the presented schemes inrealistic video streaming broadband wireless networks. Sim-ulation results show that the proposed SCEB schemes achievea superior data rate and video quality in comparison to con-ventional resource allocation schemes in cooperative cellularscenarios.

1. INTRODUCTION

The increase in multimedia traffic from applications suchas video-on-demand, mobile television, interactive gaming,etc., necessitates the enhancement of the multimedia con-tent delivery capabilities of wireless networks. Multiple-inputmultiple-output (MIMO) and orthogonal frequency divisionmultiple access (OFDMA) are attractive wireless technolo-gies for high speed networks. MIMO provides significantlyhigher throughput by spatially multiplexing several users si-multaneously for multiple access over the wireless channel[1]. Several recent works, such as [2, 3] and referencestherein, have explored optimum video transmission schemesfor MIMO based high speed wireless access. OFDMA isthe desired multiple access technique to transmit multime-dia data due to its robustness to inter symbol interference andlow complexity IFFT/FFT based implementation. OFDMAallows multiple users to access the channel by allocating spe-cific time-frequency resource to each user as shown in Fig. 1.H.264 based scalable video coding (SVC) has been demon-strated to be ideally suited for wireless video transmission dueto the dynamic quality scaling possible to meet the require-ments of users and wireless links [4] as shown in Fig. 2. In a

This work was supported by the TCS Research Scholarship program.

cellular network, the base stations (BS) are connected via highspeed links to a central base station controller. So, the BSscan cooperate to enhance the system performance through op-timal resource allocation and interference mitigation. Sucha cooperative cellular multimedia network is shown in Fig.3. In the context of cooperative multi-cell MIMO system, ablock diagonalization based downlink transmission scheme isproposed in [5] and [6]. Jafar et al. propose a dirty paper cod-ing based algorithm in [7] where new users are required tobe invisible with respect to interference to the existing users.Network coordinated beamforming techniques were suggestby [8]. However, this analysis considers a scenario which isrestrictive in nature with the solution applicable only for fewactive users and cells. Further, multimedia content is predom-inantly broadcast/ multicast in nature, where many users ora group of users subscribe for the same service. There is asignificant dearth of research which addresses optimal beam-forming and power allocation for such multicast services. In[9], the authors propose power allocation for an OFDM basedcooperative multi-cell system with single antennas at both theBSs and the users. But the model considered therein employscoherent combining of the signals from the different BSs,thus requiring additional time/ bandwidth resources. More-over, most of the above works are computationally complexand consider data rate maximization, but do not consider opti-mal allocation specifically for video quality maximization. Inthis context, we propose a novel joint successive constrainedeigenbeamforming (SCEB) and power allocation techniquefor a cooperative multi-cell MIMO network for unicast/ mul-ticast scenarios. These schemes are based on linear eigenvec-tor precoding and therefore have a low complexity comparedto existing schemes. The SCEB algorithm for unicast sce-narios to enhance the data rates, while limiting the interfer-ence with user power constraints, is employed to derive theSCEB-M for multicast scenarios to maximize the sum rateof a user group with group power constraints. Further, we de-velop user/ group scheduling schemes to choose a set of users/groups from the active subscriber set comprising of a largepopulation of users. Also, we present a framework based onrealistic H.264 video quality models to demonstrate the per-formance of the proposed algorithms in practical multimediacontent streaming cellular scenarios.

Fig. 1. Orthogonal Frequency Division Multiple Access.

Fig. 2. Scalable Video Coding (SVC).

Fig. 3. Cooperative multi-cell beamforming for multicast.

2. SYSTEM MODEL

Consider a downlink multi-cell system, employing N basestations (BS) with K users located across all the cells with Nt

antennas at each BS and Nr antennas at user terminals. Forsimplicity, we assume that the number of antennas at the dif-ferent BSs and users is same. However, this can be readily ex-tended to the case where each BS or user has a different num-ber of antennas. The base station controller (BSC) routes theunicast/ multicast multimedia traffic via a high speed back-haul to the constituent BSs and oversees cellular coordinationand power management at the BSs through cooperative trans-mit beamforming. Let Hk,n∈CNr×Nt represent the MIMOchannel matrix between user k, 1≤k≤K and base station n,

1≤n ≤N and Hk∈ CNr×NtN ∆

= [Hk,1,Hk,2, . . . ,Hk,N ]represents the concatenated channel matrix between user kand all the BSs. Thus, yk(m)∈CNr×1, the received signalvector at user k, corresponding to OFDMA time-frequencypoint m, with BS cooperation, can be expressed as,

yk(m) = Hkbkxk(m)︸ ︷︷ ︸

desired signal

+

K∑

i=1,i6=k

Hkbixi(m)

︸ ︷︷ ︸

Interference

+ η(m)︸ ︷︷ ︸

Noise

. (1)

where xk(m) denotes the symbol for user k with E{|xk|2} =1 and η(m) is the additive white Gaussian noise withη(m)∼N(0, σ2I). The quantity bk∈CNtN×1 is the aggre-

gate transmit beamforming vector for user k defined as, bk∆=

[

bTk,1,b

Tk,2, . . . ,b

Tk,N

]T

, where bk,n∈CNt×1 is the transmit

beamforming vector from BS n to user k. Let wk∈CNr×1

denote the receive beamforming vector employed by user k.Hence, rk(m), the output of the receive beamforming processat user k is given as,

rk(m) = wHk Hkbkxk(m)+

K∑

i=1,i6=k

wHk Hkbixi(m)+η̃(m).

In the next section we derive the optimal transmit and receivebeamformers to maximize the signal to interference and noiseratio at each user. In the discussion that follows we employthe notation vmax(A) to denote the unit-norm eigenvector cor-responding to largest eigenvalue λmax(A) of matrix A.

3. SUCCESSIVE CONSTRAINEDEIGENBEAMFORMING

Similar to the paradigm of successive optimization for multi-user MIMO scenarios presented in [10], we consider a suc-cessive beamforming procedure such that the transmission ofeach successively scheduled user k does not interfere withusers 1, 2, . . . , k − 1. Thus the optimal beamformer bk satis-fying the above zero interference condition while maximizingthe received signal power at user k can be computed as the so-lution to the optimization problem,

maximizebk

bHk

[

(Hk)HHk

]

bk

subject to ‖bk‖22 ≤ P,

wHj Hjbk = 0, j = 1, 2, . . . , k − 1

(2)

where P is the transmit power of the user. The zero-forcing constraints wH

j Hjbk=0 for j=1, 2, . . .,k − 1 im-ply that bk lies in the null space of Gk−1, that is,Gk−1bk = 0, where the matrix Gk−1∈Ck−1×NtN is defined

as, Gk−1∆=

[(wH

1 H1)H , (wH

2 H2)H , . . . , (wH

k−1Hk−1)H]H

.

Thus the transmit beamformer bk satisfies the conditionbk=G⊥

k−1ck, where ck∈C(NtN−k+1)×1 and the matrix

G⊥k−1 ∈ C

NtN×(NtN−k+1) is orthonormal basis for nullspace of Gk−1. Let the singular value decompositionof Gk−1 be given as U(k−1)Σ(k−1)V

H(k−1)=SVD(Gk−1).

Then G⊥k−1 corresponds to matrix of NtN−(k−1) right sin-

gular vectors G⊥k−1 =

[

vk(k−1),v

k+1(k−1), . . . ,v

NtN(k−1)

]

. Hence,

the problem given in (2) to compute the optimal beamformercan be equivalently recast by substituting the expression forbk from above as,

maximizeck

cHk (G⊥k−1)

H (Hk)HHkG

⊥k−1ck

subject to cHk (G⊥k−1)

HG⊥k−1ck = ‖ck‖22 ≤ P,

(3)

where the constraint is simplified employing the relation(G⊥

k−1)HG⊥

k−1 = INtN−k+1. The optimal solution ck ofthe above problem is the eigenvector corresponding to the

largest eigenvalue of (G⊥k−1)

H (Hk)HHkG

⊥k−1 and the cor-

responding optimal transmit beamforming vector is obtained

as bk =√PG⊥

k−1ck. Next, we describe optimal beamform-ing in multicast scenarios with group power constraints.

3.1. Multicast Scenario

Consider now a multicast scenario comprising of NG user-groups with group i, 1≤ i≤ NG consisting of Ki users.The optimal beamformer for multi-cell cooperative multicastgroup transmission of the common multicast data can be de-rived to maximize the sum-rate subject to the group powerconstraint of KiP , where the average per-user power is con-strained to be less than P , similar to unicast scenario. Letthe power loading factor of user j belonging to ith multicast

group be denoted by α(i)j , 0 ≤ α

(i)j ≤ 1. Hence the power

loading vector α(i) ∈ CKi×1 for ith MC group is

α(i) = [α

(i)1 , α

(i)2 , . . . , α

(i)Ki

]T and ‖α(i)‖22 = 1. (4)

Let the transmit beamformed vector of multicast data symbol

x(i)(m) of group i be given as∑Ki

j=1 b(i)j α

(i)j x(i)(m). The

system model for the received signal y(i)k ∈ C

Nr×1 at user k

of ith multicast group, with channel matrix H(i)k ∈ C

Nr×NtN

can be expressed as,

y(i)k (m) = H

(i)k B(i)

α(i)x(i)(m)

︸ ︷︷ ︸

Signal

+

i−1∑

l=1

H(i)k B(l)

α(l)x(l)(m)

︸ ︷︷ ︸

Interference

+

NG∑

l=i+1

H(i)k B(l)

α(l)x(l)(m)

︸ ︷︷ ︸

Zero-Forced

+ η(m)︸ ︷︷ ︸

Noise

, (5)

where B(l)∈CNtN×Kl= [b(l)1 ,b

(l)2 , . . . ,b

(l)Kl

] is the aug-mented beamforming matrix of the lth multicast group. Nat-urally, now, for successive cooperative multicast to the groupi, one has to enforce group orthogonality for progressivelyselected groups. At stage j, 1 ≤ j ≤ NG, let the optimal re-ceive beamformer for user l, 1 ≤ l ≤ Kj belonging to group

j be w(j)l . Hence, Γ(i)b

(i)k = 0, 1 ≤ k ≤ Ki where the

matrix Γ(i) is defined as,

Γ(i) =

G(1)

G(2)

...

G(i−1)

, and G(j) =

(w

(j)1

)HH

(j)1

(

w(j)2

)H

H(j)2

...(

w(j)Kj

)H

H(j)Kj

.

Hence, the optimal beamfomer satisfies the condition b(i)k =

(Γ(i)

)⊥c(i)k . The optimal transmit beamformer for user k can

be obtained as the solution of optimization problem,

maximizeck

(c(i)k

)H(

H(i)k

(

Γ(i))⊥)H

H(i)k

(

Γ(i))⊥

c(i)k

subject to ‖c(i)k ‖22 = 1.

The optimal power loading vector α(i) can be computed

as follows. From (5) the received signal power of userk of group i employing maximum ratio combining is(

H(i)k B(i)

α(i))H

H(i)k B(i)

α(i). The transmit power for

group i is (αi)H (

B(i))H

B(i)α

(i)=KiP . Thus the optimal

vector α(i) that maximizes the sum received power of the ithmulticast group can be computed as the solution of the opti-mization problem,

maximizeα

(i)

(

α(i))H

Ki∑

k=1

(

H(i)k B(i)

)H

H(i)k B(i)

α(i)

subject to(

α(i))H (

B(i))H

B(i)α

(i) ≤ KiP.

(6)

The vector α(i) is computed as α

(i) =√KiP α̃

(i) where

α̃(i) is obtained as,

α̃(i) = vmax

(∑Ki

k=1

(B(i)

)H (H

(i)k

)HH

(i)k B(i)

(B(i)

)HB(i)

)

. (7)

The proposed techniques do not require sophisticated opti-mization solvers as the optimal points are computed by eigen-value decomposition.

4. OPTIMAL RECEIVE BEAMFORMING

The receive beamformer w(i)k,MRC that maximizes the signal

power in the absence of interference is given by the maxi-

mum ratio combining beamfomer, w(i)k,MRC =

H(i)k

B(i)

α(i)

||H(i)k

B(i)α

(i)||.

However, in the proposed SCEB scheme, the optimal beam-former that maximizes the SINR can be computed as follows.Due to the successive group interference cancellation of theproposed scheme, the optimal SINR maximizing beamformer

w(i)k,MVDR for user k of group i is given by the Capon beam-

former w(i)k,MVDR =

(S(i)k

)−1H

(i)k B(i)

α(i), where the covari-

ance S(i)k is given by,

S(i)k = σ2

ηI+

i−1∑

l=1

H(i)k B(l)

α(l)(

H(i)k B(l)

α(l))H

. (8)

The corresponding beamformer for the unicast scenario canbe derived in a straightforward way by substituting Ki = 1.In the next section we describe scheduling algorithms for co-operative multicell scenarios.

5. USER SCHEDULING

The proposed SCEB schemes can be naturally employed toderive efficient schedulers for cooperative cellular network

Algorithm 1. Successive Constrained Eigenbeamforming andOptimal Power Allocation for Multicast Scenario.

Input: Channel Matrices H(i)k , 1 ≤ i ≤ NG, 1 ≤ k ≤ Ki

Output: Transmit beamformers b(i)k , receive beamformers

w(i)k , power loading vector α(i), scheduling order S .

1: for each stage r do2: Initialization: U := {1, 2, . . . , NG}, S = {}, i = 1,

j = NtN ,(Γ(1)

)⊥= I

3: while j 6= 0 & & |U| 6= 0 do4: for all i ∈ U \ S do5: for all k = 1 : Ku do

6: c(i)k ← vmax

(( (

Γ(i))⊥ )H

(

H(u)k

)H

H(u)k

(Γ(i)

)⊥)

7: Compute Optimal transmit beamformer

b(i)k ←

(Γ(i)

)⊥c(i)k

8: end for

9: R(i)(m)←∑Ki

k=11Ki

log2

(

1 + SINR(i)k

)

.

10: end for11: if Scheduling = MaxRate then12: u = arg max.

l∈U\S

R(l)(m)

13: else if Scheduling = Proportional Fairness then

14: u = arg max.i∈U

R(i)(m)

R̂(i)(m)

15: else if Scheduling = Round Robin then16: u = m mod NG

17: end if

18: Compute B(u) ←[

b(u)1 ,b

(u)2 , . . . ,b

(u)Ku

]

19: α̃(u) ← vmax

(∑Kik=1 (B

(u))H(H

(u)k

)HH(u)k

B(u)

(B(u))HB(u)

)

20: Power factor α(u) ←√KiP α̃

(i)

21: Compute the covariance matrix

Sk ← σ2ηI+

∑

l∈S H(u)k B(l)

α(l)(

H(u)k B(l)

α(l))H

22: Compute optimal receive beamforming vector

w(u)k for user k of group u,

w(u)k ← (Sk)

−1H

(u)k B(u)

α(u)

23: Update S ← S ∪ {u}, U ← U \ {u}24: Update i← i+ 1, j ← j −Ku

25: G(r) =

[(

H(r)1

)H

w(r)1 , . . . ,

(

H(r)Kr

)H

w(r)Kr

]H

26: Γ(r) =[(G(1)

)H,(G(2)

)H, . . . ,

(G(r−1)

)H]H

27: Compute ((Γ(r))⊥) as matrix of NtN −Ku+1

right singular vectors of Γ(r)

28: end while29: r ← r + 130: Compute R̂(l)(m+ 1) ← (1 − µ)R̂(l)(m) +

µIS(l,m)R(l)(m), ∀l ∈ U .31: end for

to maximize the efficiency of resource allocation to enhancethe quality of service (QoS). Consider the set of multicastgroups U := {1, 2, . . . , NG} and let S ⊂ U represent theset of the currently scheduled users. From the basic schedul-ing schemes give in [11], the Max Rate (MR) scheduler forthe SCEB can be derived as,

uMR = arg max.i∈U\S

Ki∑

k=1

1

Kilog2

(

1 + SINR(i)k

)

︸ ︷︷ ︸

R(i)(m)

, (9)

where R(i)(m) is the average rate of the ith group and the

SINR of the kth user in the group, denoted by SINR(i)k , can

be computed as,

SINR(i)k =

(

H(i)k B(i)

α(i))H (

S(i)k

)−1

H(i)k B(i)

α(i). (10)

The scheduled user set S is updated as S := S ∪ {u} andthe above procedure is repeated till the maximum schedu-lable user threshold is reached. The Round Robin schedul-ing scheme which maximizes user fairness at time-frequencypoint m is given as, uRR = m mod NG. Finally the Pro-portional fairness algorithm which aims at maximizing therate while also scheduling the users fairly is described by the

scheduler metric, uPF = arg max.i∈U

R(i)(m)

R̂(i)(m), where the group

average rate R̂(i)(m) is computed as,

R̂(i)(m) = (1−µ)R̂(i)(m−1)+µIS(i,m−1)R(i)(m−1),

where IS(i,m− 1) is the indicator function for user schedul-ing defined as IS(i,m) = 1 if the i th user group is sched-uled at time-frequency point m and zero otherwise. TheSCEB based scheduling procedure is succinctly summarizedin Algortihm 1. Further, the proposed SCEB based multiuserbeamforming schemes are ideally suited for multimedia con-tent transmission in cooperative cellular scenarios. In thiscontext a novel contribution of this work is to directly studythe performance of the proposed schemes in realistic videoscheduling scenarios. For this purpose we employ the prac-tical H.264 based video rate quality models [12]. As thesemodels are derived from the standard joint scalable videomodel (JSVM) reference codec for the H.264/SVC specifi-cation [13], they are readily applicable in practice. The rateR(q, t) in terms of the scalable quantization parameter q andthe frame rate t of the H.264 coded video is given as,

R(q, t) = Rmax

(1− e−ct/tmax

1− e−c

)

︸ ︷︷ ︸

Rt(t)

ed(1−q/qmin)︸ ︷︷ ︸

Rq(q)

,

where Rmax = R(qmin, tmax) is the maximum bit rate cor-responding to the video sequence of highest quality at themaximum frame rate tmax and minimum quantization param-eter qmin. The quantities Rq(q), Rt(t) are the normalizedrate function vs quantization parameter and frame rate respec-tively. Similarly, the scalable video joint quality function is

Sequence ai ci di βi γi Rimax

Foreman CIF 7.70 2.057 2.2070 -0.0298 1.4475 3046.30

Akiyo CIF 8.03 3.491 2.252 -0.0316 1.4737 612.85

Football CIF 5.38 1.395 1.490 -0.0258 1.3872 5248.90

Crew CIF 7.34 1.627 1.854 -0.0393 1.5898 4358.20

City CIF 7.35 2.044 2.326 -0.0346 1.5196 2775.50

Akiyo QCIF 5.56 4.019 1.832 -0.0316 1.4737 139.63

Foreman QCIF 7.10 2.590 1.785 -0.0298 1.4475 641.73

City 4CIF 8.40 1.096 2.367 -0.0346 1.5196 20899.00

Crew 4CIF 7.34 1.153 2.405 -0.0393 1.5898 18021.00

Table 1. Model Parameter for various video sequences.

(a) Akiyo (b) City

(c) Crew (d) Football

Fig. 4. Test Video Sequences.

4 8 12 16 20 24 28 32 36 40

50

100

150

200

250

300

350

400

450

500

550

Total Power(dB)

Sum

R

ate

(M

bps)

SCEB−MVDR(4x4)

SCEB−MRC(4x4)

PPA−MRC(4x4)

WPA−MRC(4x4)

SCEB−MVDR(2x2)

SCEB−MRC(2x2)

PPA−MRC(2x2)

WPA−MRC(2x2)

(a) Sum rate vs. transmit power while employingdifferent beamforming techniques.

4 8 12 16 20 24 28 32 36 40

50

100

150

200

250

300

350

400

450

500

550

Total Power (dB)

Sum

Rate

(Mbps)

MR(2x2)

PF(2x2)

RR(2x2)

MR(4x4)

PF(4x4)

RR(4x4)

(b) Sum rate of SCEB based maximum rate (MR),proportional fairness (PF) and round robin (RR)scheduling schemes.

4 8 12 16 20 24 28 32 36 4040

50

60

70

80

90

Total Power (dB)

Vid

eo

Qu

alit

y

PF(4x4)

RR(4x4)

MR(4x4)

PF(2x2)

RR(2x2)

MR(2x2)

(c) Video quality of test video sequences, shownin Fig. 4, with SCEB multicast beamforming.

Fig. 5. Sum rate and video quality with beamforming and scheduling schemes in 2 × 2, and 4 × 4 MIMO multicast scenariowith NG = 50 groups and two users in each group.

given as,

Q(q, t) = Qmax

(1− e(−at/tmax)

1− ea

)

︸ ︷︷ ︸

Qt(t)

(βq + γ)︸ ︷︷ ︸

Qq(q)

,

where Qmax = Q(qmin, tmax) is the highest quality of thevideo sequence corresponding to tmax, qmin. The quantitiesa, c, d, β, and γ are the scalable video rate and quality pa-rameters. The values of these parameters from [12] for thestandard video sequences Akiyo, Foreman, Football, City, andCrew, shown in Fig. 4 , are presented in Table 1. In thiswork, parameter qmin = 15 is chosen, parameter tmax is setas tmax = t, and the normalized value of the parameter Qmax

is set to 100. The sum rate and video quality performance ofthe proposed SCEB based scheduling algorithms in coopera-tive cellular scenarios is demonstrated in next section.

6. RESULTS

The simulation setup consists of a downlink multi-cell sys-tem with N=4 base stations serving NG=50 multicast groupswith Ki=2 users per group. The number of users is set to two

per group to show the worst case performance in the multi-cast scenario. The multicast scenario can be viewed as gener-alized model for unicast and broadcast. The unicast scenariois equivalent to multicast with one user in each group and thebroadcast scenario is equivalent to multicast scenario with allthe active users in a single group. The BSs employ Nt∈{2, 4}antennas and the mobile stations employ Nr∈{2, 4} anten-nas. We assume Nr×Nt Rayleigh fading MIMO channel be-tween each BS-user pair. We consider a OFDMA scenariowith NS=8 subcarriers and bandwidth of 1 MHz. As alreadydescribed, we consider the standard digital video sequencesAkiyo, Foreman, Football, City, and Crew [14] belonging toCIF/ QCIF/ 4CIF spatial resolutions. The characteristic scal-able video rate and quality parameters ai, ci, di, βi, γi, andthe associated maximum rate Ri

max are listed in Table 1. Weconsider a transmit power P in the range of 4≤ P≤40 dBand the noise power σ2

η is normalized to 0 dB. The propor-tional fairness weighting factor µ is set to 0.05. The sumdata rate comparison for multicast scenarios for N =4 co-operating BSs with NG=50 groups comprising Ki=2 usersper group is illustrated in Fig. 5 for 2 × 2 and 4 × 4 MIMOwireless links. As the user power increases, it leads to in-crease in the interference. Thus it can be seen from Fig.5a that SCEB-M scheme with Capon beamforming (SCEB-

MVDR) performs better in comparison with maximum ra-tio combining beamforming (SCEB-MRC) and sub-optimalschemes such as proportional power allocation (PPA) whichallocates the power to users in proportion to the strength oftheir channel, and equal power allocation (WPA). The sumrate performance of the maximum rate, proportional fairnessand, round robin scheduler for multicast scenario while em-ploying SCEB-M and Capon beamforming is given in Fig.5b. The results therein demonstrate the robust performanceof the proposed cooperative multicell beamforming schemeswhich can be seen to achieve net data rate in the range of50 to 550 Mbps. Further, the performance of the opportunis-tic maximum rate scheme is slightly higher in terms of sumthroughput compared to the competing proportional fairnessand round robin schemes. It should be noted that the achiev-able sum rate increase as more antennas are employed, due tobetter utilization of degrees of freedom available in the mul-tiuser diversity modes of the system. Further, adding moreantennas has direct bearing towards the achieved video qual-ity, which is shown in Fig. 5c. An interesting and practicalcomparison of the proposed schemes in terms of H.264 videocontent scheduling in cooperative scenarios is shown in Fig.5c. It can also be noted that the video quality rate in the trans-mit power range of 4 to 40 dB saturates at high power as aresult of increase in interference while the number of spatialmultiplexing modes remain constant. However, better videoquality can be achieved by employing SCEB beamformingwith more transmit antennas at the BS. The comparison ofvideo quality for the above schedulers in multicast scenarioclearly brings out the superiority of SCEB based proportionalfairness video scheduling in cooperative cellular scenarios.

7. CONCLUSION

A novel successive constrained eigenbeamforming schemetowards spatial multiplexing in a cooperative multicell MIMOnetwork for unicast/ multicast scenarios was presented. Thisscheme employs successive nulling based beamforming tomaximize the data rate while limiting the interference. Fur-ther, the successive constrained eigenbeamforming was ex-tended to sum rate maximization in a multicast scenario withgroup orthogonality and power constraints. The proposedbeamforming has been employed as a basis to derive ef-ficient schedulers for unicast/ multicast scenarios. Perfor-mance comparison of the proposed transmit eigenbeamform-ing with receive Capon beamforming demonstrated superiorperformance and its suitability for cooperative cellular sce-narios. Further, specifically in the case of scalable videobased multimedia content, we demonstrated enhanced enduser video quality employing realistic H.264/ SVC rate andquality model derived from the JSVM reference codec.

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