QoE-aware Mobile Association and Resource Allocation ... - IEEE Xplore

Globecom 2014 - Wireless Networking Symposium

QoE-aware Mobile Association and Resource Allocation Over Wireless Heterogeneous Networks Yiran Xu, Rose Qingyang Hu

Lili Wei, Geng Wu

Department of Electrical and Computer Engineering Utah State University, Logan, UT, USA Emails: [email protected], [email protected]

Mobile and Communications Group Intel Corporation, OR, USA Email: [email protected], [email protected]

Abstract—As video content delivery over wireless networks is expected to grow tremendously in upcoming years, how to support energy and bandwidth consuming video applications with high Quality of Experience (QoE) becomes a challenging issue in the future wireless networks. Clearly, there is an urgency for a new disruptive paradigm to bridge the gap between the increasing capacity plus energy demands and the scarce wireless network resources. In this paper, we propose a spectrum and energy efficient mobile association and resource allocation scheme in wireless heterogeneous networks based on two new performance metrics: video content aware or QoE-aware Energy Efficiency (QEE) and video content aware or QoE-aware Spectral Efficiency (QSE). QEE and QSE evaluate the power consumption and bandwidth consumption from video quality’s perspective. First we conduct a fundamental study between QEE and QSE in a point-to-point wireless environment. Then we propose a mobile associations and resource allocation scheme in a heterogeneous wireless network that can optimize QEE and QSE. The system level problem is formulated as a mixedinteger nonlinear optimization problem. Nonlinear fractional programming approach and dual decomposition method are applied to search the optimal solutions in a computationally efficient way. Introduction∗

I. I NTRODUCTION The explosive increase of video applications and handset devices accelerates the demands for highly spectrum and energy efficient wireless networks. Mobile devices, such as smart-phones and tablets, are widely used to conduct video chatting, video streaming, music and movies downloading, etc. According to [1], in year 2012, mobile video traffic exceeds 50% of the total wireless traffic volume for the first time. Mobile video will increase 16-fold between 2012 and 2017, accounting for over 66% of total mobile data traffic by the end of 2017. The high data volume boosted by video applications results in the explosive growth of energy and network resource consumptions. Within traditional cellular networks, the base station (BS) consumes large amount of energy to support the mobile users (MUs), especially the cell edge users. Wireless heterogeneous network [2], [3] introduces a hierarchical framework, where the high power BSs generate large cells to provide blanket coverage and seamless mobility, while the ∗ This work was partially supported by National Science Foundation grants ECCS-1308006.

978-1-4799-3512-3/14/$31.00 ©2014 IEEE

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low power devices like femto-/pico-BS generates small cells to help support cell-edge users and boost local capacity. Since these small cells are usually located at the coverage holes or capacity-demanding hotspots, they extend the wireless service coverage range and enhance the cell-edge performance, so that the traffic load pressure is reduced on the BSs. There has been a substantial amount of work in the areas of mobile association and resource allocation. A detailed discussion on mobile association schemes with load balancing in cooperative relay networks is presented in [4]. The optimal mobile association as well as resource allocation schemes to improve the system performance and users’s quality with a comprehensive consideration on the resource consumption at the backhaul link are explored in [5]-[9]. However, the aforementioned work mainly focused on cellular networks with traditional data applications. There is a lack of works that solve the problems for users with video applications. In this paper, we propose two new system performance metrics, QSE and QEE, which measure the video quality per unit network resource consumption and power consumption. The two performance metrics are evolved from the traditional spectral efficiency (SE) [10], [11] and energy efficiency (EE). Taking into consideration that not very bit of the video content is of the same importance and priority for delivery, we use video peak-signal-to-noise-ratio (PSNR) to characterize video QoE, based on which QSE and QEE are defined as PSNR/Hz and PSNR/Watt respectively. We first study QSE and QEE in a point-to-point (PtP) wireless link. Then, we formulate a joint mobile association and resource allocation optimization problem in a wireless heterogeneous network. The problem is solved by applying nonlinear fractional programming and dual decomposition techniques. The proposed methodology aims to achieve a good balance between the computational complexity and optimality. The remainder of this paper is organized as follows. Section II provides preliminary knowledge about wireless heterogeneous network model and defines the new concepts of video performance metrics. QEE and QSE in PtP additive white Gaussian noise (AWGN) channels are investigated in Section III. System level QSE and QEE are studied by formulating an optimal mobile association and resource allocation problem in Section IV. In Section V we develop a nonlinear fractional programming and the dual decomposition method to solve the


where α is a predefined parameter that is related to video content and R is the achievable data rate over wireless channels.

C. QoE-aware Spectral Efficiency and Energy Efficiency

Fig. 1: Video transmission over wireless heterogeneous network

problem with computational efficiency. Simulation results and discussions are presented in Section VI. Finally, we conclude our work in Section VII. II. V IDEO C ONTENT D ELIVERY OVER W IRELESS H ETEROGENEOUS N ETWORK

A. Wireless Heterogeneous Network We consider a two-tier wireless heterogeneous network shown in Fig. 1, where in each macro-cell, there exists one macro BS (MBS) and several overlaid pico BSs (PBSs). Typically, a PBS has a much lower transmit power and a picocell has a smaller coverage range compared with a macro-cell. We denote the transmit power for a MBS and a PBS as Pm and Pp , respectively. There are Nm macro-cells in the system and in each macro-cell there are one MBS and Np PBSs. In total Nu MUs are uniformly distributed in the network, while each MU can be served by either an MBS or a PBS, depending on the location and service requirement of the MU. The overlaid pico-cells reuse the same spectrum of the macrocell and aim to provide services with shorter communication distances, mainly at hotspots and coverage hole, such that the overall system SE, EE and coverage are greatly improved.

B. Video Quality Measurement Our study will focus on the the video content delivery in wireless heterogeneous networks. Specifically, we consider video downloading services, i.e., the MBSs and PBSs transmit the video contents to the MUs upon downloading requests. With modern video coding technologies (e.g., scalable video coding [12]), the video contents can be encoded into multiple layers, with different layer carrying contents at a different priority. The number of the video layers that are received reflects the perceived video quality at MU side, which often refers to QoE. PSNR is commonly used as a video QoE metric to measure the perceived video quality, which can be expressed as [13] PSNR = α log10 (R), (1)

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In modern wireless communications, the system design mainly focuses on achieving desirable SE and EE. There have been extensive research works done in this realm. Recent advances in video communications bring in new challenges. First, wireless network design traditionally is not contentaware. Each bit transmission contributes equally to the network throughput and is provisioned to MU QoS. For video traffic, different frames may have different importance and can contribute differently in terms of the distortion perceived at the receiving side [12]. Content-awareness can be utilized to improve the MU QoE. Secondly, since not each frame contributes the same to the MU QoE, frames with different priorities should be treated differently when allocating bandwidth and energy. Conventional SE and EE metrics are no longer adequate for the system design with content aware video applications. Thus, it is necessary to analyze SE and EE from video quality’s perspective. Without loss of generality, we denote the total bandwidth consumption as W and the total power consumption as P . The QSE and QEE are defined as PSNR ∆ PSNR and QEE = , (2) θ W Pβ where θ and β are the decaying factors that respectively indicate the relative cost of the bandwidth consumption and power consumption when delivering PSNR. A higher θ or β value means that wireless network is less concerned on PSNR but more concerned on bandwidth or power consumption. Thus the decaying factors in QSE and QEE aim to balance between the gain on PSNR value and the cost on bandwidth/power consumptions. ∆

QSE =

III. QSE

AND

QEE

IN

P T P AWGN C HANNELS

First we conduct the fundamental study between QSE and QEE in a PtP video transmission with AWGN. The total power consumption P consists of two parts: transmit power Pt and circuit power Pc . According to Shannon’s Theory, we have Pt h , (3) R = W log2 1 + W N0 where h is the channel gain and N0 is the AWGN noise density. With (1)-(3), we have h i Pt h α log W log 1 + 10 2 W N0 PSNR = , (4) QSE = θ θ W W h α log10 W log2 1 + PSNR = QEE = β Pβ (Pt + Pc )

Pt h W N0

i

.

(5)


values so that the QSE-W and QEE-P curves decrease. At the high bandwidth/power region, we noticed that both QSE-W and QEE-P curves become flat when the decaying factors are small. Remember that a smaller decaying factor indicates a lower cost on network resource W or P . So with a small decaying factor, both PSNR and W θ (P β ) increases very slowly at high W θ (P β ) region, making QSE-W and QEEP curves flat.

16

QSE(dB/kHz)

14 12 10 8 6 4 0 10

θ=0.15 θ=0.20 θ=0.30 Peak 1

10

2

Bandwidth W(kHz)

10

3

10

IV. QSE

Fig. 2: QSE performance under different decay factors θ (α = 10, SE = 8bps/Hz) 9 8

QEE(dB/mWatt)

7 6 5 4 3 2 1 10

β=0.20 β=0.30 β=0.40 Peak 2

10 Power P(mWatt)

3

10

Fig. 3: QEE performance under different decay factors β (α = 10, Pc = 8bps/Hz)

As PSNR is a logarithm function of the video throughput, any further increase for an already high video throughput will only lead to a marginal increase on the PSNR value, which is consistent with the understanding that layer-1 video frames with the lowest data rate contributes the most to PSNR. The higher the video frame layer, the smaller PSNR the frame contributes, making the bandwidth/power consumption content aware possible. Fig. 2 and Fig. 3 illustrate the impacts of decaying factors on the QSE and QEE performance, respectively. Specifically, Fig. 2 shows the QSE-W curves at different θ values and Fig. 3 shows the QEE-P curves at different β values. When θ (β) value is larger, QSE (QEE) achieves its peak value at a smaller W (P ), a lower PSNR and thus a lower peak QSE (QEE). Since a larger decaying factor tends to be more stingy on bandwidth or power consumption, the resulting PSNR is thus low. When θ and β decrease, the cost of bandwidth and power consumptions also decreases. Correspondingly the system can get a relatively higher PSNR with a higher bandwidth/power consumption. We further explains why QSE/QEE is a bell-shape curve respect to W /P as follows. At the low bandwidth/power region, the increment of PSNR is faster than the increment of bandwidth and power consumptions, so that the QSE-W and QEE-P curves go up. After the QSE-W and QEE-P reach their respective peak values, the increment of bandwidth and power consumptions surpasses the increment of the PSNR

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AND

QEE

IN THE

S YSTEM L EVEL

We have studied the fundamental QSE and QEE performance in the PtP AWGN channel. In this section, we extend our QSE and QEE study into the system level. We will apply the proposed QSE and QEE to formulate the mobile association and resource allocation problem in a wireless heterogeneous network, where a critical step in achieving efficient resource management is to associate MUs with proper serving BSs in order to fully exploit the network capacity/coverage/energy efficiency across different cell types. Most of the existing mobile association studies in heterogeneous wireless networks, if not all of them, have been based on traditional SE and EE performance metrics [5]-[9], meaning that the video contents are transparent to the mobile association process. This paper aims to make the mobile association process content aware so that the network can more efficiently exploit capacity/coverage/energy/QoE gains altogether. Define binary variables, xi,j,k , i = 1, · · · , Nm , j = 0, · · · , Np , k = 1, · · · , Nu , to indicate MU’s association status, ( 1; if MU k is associated with MBS i xi,0,k = 0; otherwise   1; if MU k is associated with PBS j   xi,j,k = in macro-cell i    0; otherwise. Furthermore, variables ni,j,k , i = 1, · · · , Nm , j = 0, · · · , Np , k = 1, · · · , Nu are defined to represent the network resources allocated to each MU. ni,0,k : ni,j,k :

radio resources assigned to MU k if associated with MBS i; radio resources assigned to MU k if associated with PBS j in macro-cell i.

To model a video content delivery network, where each MU receives video transmission from its serving node, the system level mobile association and resource allocation aims to optimize QSE and QEE. Thus the decision consists of two parts for each MU: which BS to associate (the value of xi,j,k ), and resources reserved for the video delivery at the association stage (the value of ni,j,k ). We can formulate the system level mobile association problem as P1 in (6) on the top of this


P1 : max QSE

Q(n, x) PSNR PSNR = = D(n, x) Wθ (ρm Wm + ρp Wp )θ PNm PNp PNu j=0 i=1 k=1 xi,j,k αk log10 (ni,j,k log2 (1 + γi,j,k )) P θ . P PNm PNp PNu Nm Nu ρm i=1 x n + ρ x n i,0,k i,0,k p i,j,k i,j,k j=1 k=1 i=1 k=1

= =

V. N ONLINEAR F RACTIONAL P ROGRAMMING D ECOMPOSITION M ETHOD

page subject to the following constraints: Nu X

k=1 Nu X

xi,0,k ni,0,k

≤ Cim

∀i

(7)

xi,j,k ni,j,k

p ≤ Ci,j

∀i, j

(8)

≤ 1

∀k,

(9)

k=1 Np Nm X X

xi,j,k

i=1 j=0

where n and x represent the vectors of ni,j,k and xi,j,k , respectively; ρm and ρp specify the relative cost of the bandwidth consumption at MBS and at the PBS. For the purpose of system load balancing, ρm > ρp to encourage more MUs to associate with the PBSs. (7) and (8) are the radio resource constraints at the MBSs and PBSs, respectively. (9) ensures one MU can at most associate with one MBS or one PBS. γi,j,k is denoted as the signal-to-interference-plus-noise ratio (SINR), which is defined as Pm hi,0,k

γi,0,k = N0 +

Nm X

h

i′ ,0,k

i′ =1,i′ 6=i

Pm +

Np Nm X X

, h

i′ ,j ′ ,k

Pp

i′ =1 j ′ =1

N0 +

Np Nm X X

i′ =1 j ′ =1 (i′ ,j ′ ) 6=(i,j)

hi′ ,j ′ ,k Pp +

Nm X

,

(11)

hi′ ,0,k Pm

i′ =1

 β Np Nu Nm X X X Pβ =  xi,j,k (Pc + Pt0 ni,j,k ) ,

(12)

i=1 j=0 k=1

where Pt0 is the unit transmit power per network resource.

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D UAL

A. Nonlinear Fractional Programming To facilitate the algorithm derivation, we express the objective function as Q(n, x) PSNR = . (13) max QSE = Wθ D(n, x) Without loss of generality, we define Sn as the feasible solution set of n and Sx as the feasible solutions of x. Then P1 is given by Q(n∗ , x∗ ) Q(n, x) = max ∗ ∗ D(n , x ) n∈Sn ,x∈Sx D(n, x)

(14)

According to the theorem in [14], q ∗ can be obtained if and only if (iff) P2 : F (q ∗ ) =

where hi,0,k and hi,j,k , j > 0 represent the large scale channel gains between MBS i and MU k, between PBS j in macro-cell i and MU k, respectively. Due to the space limitation, we only formulate the QSEbased optimization problem in detail in the following section. The QEE-based optimization can be formulated in a similar way, except the following: (i) the objective is to maximize QEE and (ii) D(n, x) expression is replaced by the power consumption:

AND

The optimization problem P1 is a mixed-integer nonlinear non-convex combinatorial optimization problem. The nonlinearity and non-convexity come from the fractional formulation of the objective function and the combinatorial nature comes from the binary association decision variables. It is difficult to solve the problem due to its high computational complexity, especially when considering a large number of decision variables as in this case. Therefore, we first apply the nonlinear fractional programming method [14] to simplify the original optimization problem and then use an iterative approach to solve the transformed problem in a computationally efficient way.

q∗ =

Pp hi,j,k

γi,j,k =

(10)

(6)

max

n∈Sn ,x∈Sx ∗ ∗

Q(n, x) − q ∗ D(n, x)

= Q(n , x ) − q ∗ D(n∗ , x∗ ) = 0

(15)

Till this point, we transform the original optimization problem from a fractional form to a subtractive/additive form. Equivalently, we can obtain the optimal solution for P1 by solving P2, which presents a much more computationally nicer form. We further find that requiring F (q ∗ ) = 0 is a very stringent requirements. For computation purpose, we use a very small positive value ǫ that can be denoted as the convergence criteria, i.e., F (q ∗ ) ≤ ǫ. The method is summarized in Algorithm 1. Algorithm 1 requires to jointly solve the optimal resource allocations n and mobile association decisions x with a given q. The joint optimization problem is formulated as follows: P2 :

max

n∈Sn ,x∈Sx

Q(n, x) − qD(n, x)

(16)


B. Low-level Sub-problem

Algorithm 1 Nonlinear Fractional Programming 1:

2: 3: 4: 5: 6: 7: 8: 9: 10: 11: 12: 13:

Initialize q = 0, ǫ > 0 as the convergence criteria, i = 0 is the iteration index and Maxi as the maximum iteration number. Convergence = false. while Convergence = false & i < Maxi do Update iteration index i = i + 1 Q(n,x) Update q = D(n,x) Insert q back to F (q) Solve maxn∈Sn ,x∈Sx F (q) = Q(n, x) − qD(n, x) if F (q) < ǫ then Convergence = true. n∗ = n, x∗ = x and q ∗ = q end if end while Output n∗ , x∗ and q ∗

subject to (7)-(9). For a given q, P2 is classified as a mixed-integer nonlinear optimization problem that combines the combinatorial difficulty of optimizing discrete association variables with the challenges of optimizing resource allocation. It is an NP-hard problem. Although there exist traditional approaches such as brute force and branch-and-bound methods to search the global optimal solutions, it is nearly infeasible to solve it in real time for a large scale system. Therefore, we further introduce the Lagrange dual decomposition method to solve the complex optimization problem, leading to a reasonable computational complexity. First we relax the integer variable xi,j,k to a real one in [0, 1], which can be used to indicate the association probability. We introduce an auxiliary variable n ˆ i,j,k = xi,j,k ni,j,k that can be considered as the actual radio resource allocation. Although the relaxation will approximate the optimality of the original problem, it reduces computational complexity greatly. And it is easy to observe that the relaxed optimization problem is concave on n ˆ i,j,k . Due to concavity and the affine constraints, the Slater’s condition holds [16]. Thus the strong duality is satisfied for P2. We can solve the primal problem by solving its dual. By introducing Lagrange multipliers λi > 0, µi,j > 0, and νk > 0, the Lagrange function is formulated in (17) as shown on the top of next page. With respect to the primal optimization problem P2, its dual problem is given by D2 : min sup L(ˆ n, λ, µ, ν). λ,µ,ν

(18)

In our case, the low-level sub-problem is to solve the following maximization problem for the given Lagrange multipliers max L(ˆ n, λ, µ, ν).

With KKT conditions [16], we can obtain the optimal resource allocation for MU k: #+ " α k , (20) n ˆ ∗i,0,k = xi,0,k n∗i,0,k = xi,0,k θ−1 ln 10 qθρm Wt−1 + λi " #+ αk ∗ ∗ n ˆ i,j,k = xi,j,k ni,j,k = xi,j,k , (21) θ−1 ln 10 qθρp Wt−1 + µi,j where [x]+ consumption derivative of obtain ∂L ∂xi,0,k ∂L ∂xi,j,k

= max{0, x} and Wt−1 is total bandwidth at the previous iteration. Then by taking the the sub-problem with respect to xi,j,k , we can =

=

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αk log10 n∗i,0,k log2 (1 + γi,0,k )

θ−1 − qn∗i,0,k θρm Wt−1 − λi n∗i,0,k − νk , (22) αk log10 n∗i,j,k log2 (1 + γi,j,k ) θ−1 − qn∗i,j,k θρp Wt−1 − µi,j n∗i,j,k − νk . (23)

In order to satisfy the constraint that each MU can at most associate with one MBS or one PBS, the optimal mobile association decision for MU k is then given by ( , ∀i, j 1; for {i, j} = arg max ∂x∂L ∗ i,j,k (24) x(i,j,k) = 0; otherwise

C. High-level Master Problem The high-level master problem is to solve the Lagrange multipliers. In our case, the dual function is given by g(λ, µ, ν) = L(n∗ , x∗ , λ, µ, ν).

(25)

Since g(λ, µ, ν) is differentiable, we can solve the master dual problem with a gradient descent method. The Lagrange multipliers can be updated by " ! %#+ Nu X m ∗ ∗ λi (t + 1) = λi (t) − η1 Ci − xi,0,k ni,0,k (26) k=1

µi,j (t + 1) =

ˆ n

For simplicity, we denote λ, µ, ν as the vectors for multipliers λi , µi,j , and νk , respectively. We can further decompose the dual problem distributively into a low-level sub-problem and a high-level master problem [15].

(19)

ˆ n

νk (t + 1) =

"

µi,j (t) − η2



!



p Ci,j

νk (t) − η3 1 −

−

Nu X

k=1

Np Nm X X i=1 j=0

x∗i,j,k n∗i,j,k

%#+

(27)

+

x∗i,0,k  , (28)

where t is the iteration index and (η1 , η2 , η3 ) are the positive step sizes.


L(ˆ n, λ, µ, ν) =

Q(ˆ n) − qD(ˆ n) −

Nm X

λi

i=1

−

Nu X

k=1

−

−





νk 

q ρm

Np Nm X X i=1 j=0

Nm X Nu X

i=1 j=1

µi,j

n ˆ i,0,k −

n ˆ i,0,k + ρp

!N u X



k=1

%

57 macro-cells

Pico-Cell

4 per macro-cell

Mobie Users

300 MUs per cell

Circuit Power

Pc = 13dBm

Transmit Power

Pm = 46dBm, Pp = 30dBm

System Bandwidth

20MHz

Noise Model and density

AWGN, -174dBm/Hz

D. Iterative Optimization between Low-level and High-level We can solve the dual problem by solving the low-level and high-level problems iteratively. We feed the updated multipliers’ solutions from the high-level master problem to the low-level sub-problem. Then with the optimal solutions of resource allocation and mobile association in the low-level problem, we can update them back to the master problem and re-calculate the multipliers. When the iteration converges, the dual problem is considered solved. We can go back to Algorithm 1 to find out the optimal q ∗ . VI. P ERFORMANCE S TUDIES In this section, we conduct the simulations following the 3GPP case 1 configurations specified in [17]. We consider a 19-cell 3-sector three-ring hexagonal network structure, where one MBS is located in the center of macro-cell, and 4 PBSs are equally-distanced deployed in the overlaid pico-cells in each macro-cell, which form a two-tier heterogeneous network. MUs are uniformly distributed in the network. Detailed parameter settings are shown in Table I. Fig. 4(a) and Fig. 4(b) illustrate the average PSNR values in the system at different decaying factors. With a larger decaying factor, the system is not cost-efficient to improve the QSE/QEE performance by increasing the PSNR values. Hence, it is not difficult to understand the average PSNR value

4700

θ

k=1

νk 

Nm X

λi

i=1

Np Nm X X i=1 j=0

!N u X

n ˆ i,j,k −

p Ci,j

k=1

αk xi,j,k log10



Avg. PSNR (dB)

Macro-Cell

−

Nu X

µi,j

i=1 j=1

n ˆ i,j,k  −

Avg. PSNR (dB)

TABLE I: Simulation Parameters Settings

−

Np Nm X X

i=1 j=0 k=1

Np Nu Nm X X X

p n ˆ i,j,k − Ci,j

%

Np Nu Nm X X X

i=1 j=1 k=1

Parameter

Cim

k=1

xi,j,k − 1 =

i=1 k=1

Np Nm X X

!N u X

%

n ˆ i,j,k log2 (1 + γi,j,k ) xi,j,k

!N u X

n ˆ i,0,k − Cim

k=1

%



xi,j,k − 1

(17)

(a) Average PSNR at different decaying factors θ 44 43 42 41 0.01

0.02

0.03

0.04

0.05

0.2

0.3

0.4

0.5

0.06

0.07

0.08

0.09

0.1

0.6

0.7

0.8

0.9

1

θ (b) Average PSNR at different decaying factors β

40 35 30 25 0.1

β

Fig. 4: Average PSNR at different decaying factors:(a) QSE optimized; (b) QEE optimized

is low at high decaying factor. Once the decaying factor is reduced, the system can improve its QSE/QEE by consuming more bandwidth and power with an acceptable cost to increase the PSNR values. Therefore, it can be observed that the PSNR values are relatively higher when the decaying factor is lower. Fig. 5 depicts the PSNR distributions with different decaying factors. Taking Fig. 5 (b) as an example, it can be observed that the system with a lower power decaying factor has a better PSNR distribution. It is shown that the curves of β = 0.1 has an approximate 15dB gain on the PSNR value over the curve of β = 1.0. With smaller decaying factors, more MUs can receive a higher PSNR due to the relatively low cost of resource consumptions. When the decaying factor is large, the system is stingy with the bandwidth and power consumption. Thus, MUs are discouraged to consume more resources to increase the PSNR values. In Fig. 6 and Fig. 7, we analyze the impacts of weight factors ρm and ρp on MBSs’ and PBSs’ resource utilization. As the ρm /ρp varies from 2 to 10, it is noted that the MBS’s utilization decreases from 19% to 7% (Fig. 6(a)). While the PBS’s utilization increases from 30% to 88% (Fig. 6(b)). At the same time, more MUs choose to associate with the PBSs (Fig. 7(b)), going from 61% at ρm /ρp = 2 up to 73% at


(b) PSNR CDF at different β

1

0.8

0.8

Probability

Probability

(a) PSNR CDF at different θ

1

0.6

0.4 θ =0.01 θ =0.03 θ =0.06 θ =0.10

0.2

0 0

20

40

60

PSNR(dB)

0.6

0.4 β =0.1 β =0.3 β =0.6 β =1.0

0.2

0 0

80

20

40

60

PSNR(dB)

80

Fig. 5: PSNR CDF at decaying factors: (a) QSE-optimized; (2) QEE-optimized (b) PBS utilization, θ = 0.1 90

18

80

16

70

Utilization(%)

Utilization(%)

(a) MBS utilization, θ = 0.1

20

14 12 10 8 6 2

R EFERENCES

60 50 40 30

4

6

ρm/ρp

8

20 2

10

4

6

ρm/ρp

8

10

(a) MUs associated with MBS, θ = 0.1 40

(b) MUs associated with PBS, θ = 0.1 74

38

72

36

70

Portion (%)

Portion (%)

Fig. 6: Portion of total MUs associating with MBS and PBS at different ρm /ρp

34 32

68 66

30

64

28

62

26 2

4

6

ρm/ρp

8

10

60 2

wireless heterogeneous network. We started the paper with the fundamental study between QSE and QEE in PtP AWGN channels. We further formulate a joint optimization problem of mobile association and resource allocation in a video wireless heterogeneous network. The optimization problem is solved by using a nonlinear fractional programming and dual decomposition technique in a computationally efficient way. The simulation results show that by considering QoE in spectrum efficient and energy efficient wireless network design, the system performance including PSNR distribution and resource utilization greatly depends on the bandwidth and power decaying factors, which motivates us to seek more understandings on how the decaying factors are related to the bandwidth and power consumption pricing models. Also in the future, the work can be extended to explore the tradeoff study between QSE and QEE.

4

6

ρm/ρp

8

10

Fig. 7: MBS and PBS resource utilization at different ρm /ρp

ρm /ρp = 10. While at the same time, the portion of the total MUs attaching to the MBSs drops from 40% to 27% (Fig. 7(a)). This is because that with higher ρm /ρp ratio, the MU associating with the MBS has higher bandwidth consumption cost. The higher resource cost will encourage more MUs to associate with the PBS which has relatively lower cost. In this way, the system is load-balanced. Considering the fact that PBSs are usually low power-driven, the system can be energy-saving and achieve energy-efficiency goal. VII. C ONCLUSIONS In this paper, we developed QoE-aware spectrum efficient (QSE) and energy efficient (QEE) mobile association and resource allocation scheme for video content delivery in a

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