An On-Demand QoE Resource Allocation Algorithmfor Multi-flow LTE eMBMS

Hsiang-Yun Meng1, Ching-Chun Chou2, Rafael Kaliski3, Hung-Yu Wei4Department of Electrical Engineering

National Taiwan Universityr02921057@ntu.edu.tw1, f95921098@ntu.edu.tw2, rkaliski@ieee.org3, hywei@ntu.edu.tw4

Abstract—Recent multimedia studies show that the Quality-of-Experience(QoE) more accurately represents the user’s level ofsatisfaction than the Quality-of-Service (QoS) does. Long TermEvolution (LTE) Multimedia Broadcast Multicast Operation(MBMS), via MBMS Operation On-Demand (MooD), enablesdynamic resource configuration of multicast flows. In order tomaximize the QoE of all users in a LTE MooD system wepropose a resource allocation method which efficiently allocatesresources based on both the demand of each live video streamand the channel conditions of the users. We compare our methodagainst other methods; our method achieves the highest QoE anddemonstrates efficient resource allocation regardless of wheneverresources are sufficient or not.

I. INTRODUCTION AND RELATED WORKS

Recent multimedia distribution schemes emphasize end-users’ Quality of Experience (QoE) metrics over traditionalperformance metrics, such as throughput or error rate. Due tothe time varying wireless channel conditions experienced byend-users, several works analyze the varying channel condi-tions problem in terms of both multicast and broadcast services[1]. In terms of QoE related research, Dobrian et al. [2] utilizeddata-mining to analyze the relationship between video qualityand user engagement. Their research found that a video’sbitrate and its buffering ratio dominates a system’s QoE, ascaptured by their user engagement metric. Based on Dobrian’sresults [2], [3] and [4] derived QoE functions for both wiredand wireless multicast systems.

3GPP (3rd Generation Partnership Project) recently decidedthat LTE MBMS (Multimedia Broadcast / Multicast Service)Operation On-Demand (MooD) [5] should support Over-the-Top (OTT) multimedia service. MooD enables on-the-flyMBMS service configuration and seamless service migration.For example, when it becomes more efficient to run a unicastservice as a MBMS service, the system may activate a previ-ously inactive MBMS session for the service. As such, futureLTE MBMS services may be dynamic and configured basedon each user’s requirements and/or the system’s preferences.

To enable active dynamic MBMS configuration, LTE re-source allocation is further studied in this paper. As shownin figure 1, Resource Blocks (RBs) have a duration of 0.5milliseconds and a width of 180kHz; RBs are the basicresource allocation unit used for both unicast and multicastservices. In this paper we investigate multiple methods of RBallocation based on each users’ demand and their respectivechannel conditions.

To enhance the QoE of LTE MooD multimedia services,we propose a QoE-based algorithm for LTE MBMS resource

4corresponding author

Frequency

Time

Totally 12 subcarriers

12 subcarriers

= 1 carrier (180kHz)

1 Resource Block 1 Symbol

2 slots = 1 subframe(1 millisecond)

Fig. 1. LTE time-frequency radio resource

allocation. In our proposed algorithm, the RBs are dynamicallyallocated to different video flows based on the system objectiveof reducing the buffering ratio and increasing the average bit-rate. Our algorithm maximizes the QoE of the LTE MooDmultimedia services.

II. PROBLEM FORMULATION

In this section we first briefly introduce our objective func-tion, the QoE utility, then we present the OTT live streamingresource allocation problem.

A. Features of QoE Utilities

The most important parameters in the user engagement-oriented QoE utility function [2] are the buffering ratio (buffer-ing ratio is the percentage of time spent re-buffering a video)and the average bitrate. The higher the buffering ratio is, theless likely the viewers are to watch the video for an extendedperiod of time. The higher the average video bitrate is, themore likely the viewers are to watch the video for a longerperiod of time.

The utility in [3], shown in equation (1), is a linear functionbased on buffering ratio and average bitrate. The utility pre-sented in [4], shown in equation (2), is an exponential utilityfunction based off the same data from [2]. The linear utilityfunction, while simpler, becomes distorted when the bufferingratio exceeds approximately 10%, this is due to the limitationsof linear-regression. In contrast to the linear utility function,the exponential utility function more accurately represents theoverall user engagement curve.

Ulinear = −3.7×BuffRatio+Bitrate

20(1)

Uexp = V ideoLength×RBitrate ×RBuffRatio (2)

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eNB

UE1 UE2 UE3 UE4 UE5 UE6

Group 1 Group 2 Group 3

Fig. 2. Example of 3 Multicast Video Group

where RBitrate and RBuffRatio are:

RBitrate = 1− e−0.0001024×AvgBitrate

RBuffRatio = e−0.04606×BuffRatio×100

In Eq.(1), a buffering ratio increase of 1% results in adecrease of user engagement by 3.7 minutes; while an averagebitrate increase of 20kbps results in the user engagementincreasing by 1 minute. Similarly, equation (2) shows that anincrease in the buffering ratio or a decrease in the averagebitrate results in an exponential decrease of the utility. Noticethat the buffering term dominates the utility function as [2]showed that for a live video the buffering ratio is the mostsignificant factor in determining user engagement, while videobitrate is the second most significant factor in determining userengagement .

B. Problem Formulation

In this work we consider a single cell LTE eMBMS sce-nario. In this scenario, which is based on [6], the surroundingcells act as static interference sources with frequency flatdistributions.

In our problem formulation there are multiple user equip-ments (UEs) in the cell. Each UE subscribes to a single videostream. UEs which subscribe to the same video are classified asbelonging to the same video / multicast group. Figure 2 showsan example of 3 separate multicast groups. Each multicastgroup may have multiple channels on which it can receivedata. Due to the nature of multicast, all the members of agroup share the same coding scheme on any given channel /carrier.

There are two critical factors used for resource allocation:the each video packet’s size / video packet’s deadline and eachUE’s channel conditions. As all videos are streamed live, theEvolved Node B (eNB) distributes the multimedia content onthe fly, i.e. the eNB is unable to obtain information such aspacket size until the live streaming data is generated. This isa general feature of live streaming. We assume that the eNBcan obtain the downlink quality of all carriers used by eachUE.

The packet size of a video i is denoted as b[i]Fnow

. Thechannel conditions of the kth user of video i in channel j aredenoted as m[i]

j,k. For convenience, all symbols with superscript[i] indicate that they correspond to the video group [i].

We formulate the resource allocation problem as a multi-round integer optimization problem. In each round the eNBruns our algorithm and determines a resource allocation foreach multicast group, which maximizes the system utility.The resulting allocation, Xji, indicates which channel, j, is

allocated to which group, i. Our method maximizes the systemutility, which results in an optimal user QoE. All notations usedin this paper are contained in table I.

Vari

able

s Symbol DescriptionXji the j-th channel is allocated to video group [i]

X the decision variable at each round, an array of {Xji}S[i] trunk size for video group [i] at one moment

Inpu

t

m[i]j

the group MCS of group [i] in j-th channel , define in Eq.(4)

m[i]

j,kthe MCS of user-k of video group [i] in j-th channel

b[i]

Fnowthe packet size of the current frame of video group[i]

V [i] s set of members of video group [i]

V number of video groupN [i] the population of video group [i], N [i] = |V [i]|NRB number of carriers(channels) / resource blocks (RB) in this systemN

[i]

buffnumber of buffering events of group [i]

Bits[i] number of total bits which has been transmitted for group [i]

T a given period that an allocation will be used for

Func

tions U [i] utility of the video group [i]

U utility of the system (i.e. utility of this eNB)M(m) a mapping function which maps MCS m to Bitrate per RBg(n) a weighted function to weight a group with n users

Con

st wre coefficient of Re-Buffering term in utility, wre = −3.7wkb coefficient of Avg.Bitrate term in utility, wkb = 0.05

wb the same as wkb while the unit is in bit

TABLE I. TABLE OF NOTATIONS

We need to determine the bandwidth based on the trunksize, S[i], allocated to each group, [i], in order to determineeach group’s corresponding average bitrate and buffering ratio.Given a channel allocation, Xji, the trunk size allocated to avideo is defined per equation (3).

S[i] =

NRB∑j=1

Xji ×M(m[i]j )× T (3)

Where the selected modulation coding scheme (MCS) usedfor channel-j, assigned to video [i], is m

[i]j .

m[i]j = min

k∈Vi

m[i]j,k (4)

The MCS assigned to the group is dependent on thegroup member(s) with the worst channel conditions. M(m

[i]j )

represents a mapping function which maps each MCS to itscorresponding per RB bitrate.

In the next section we discuss our resource allocationmethod.

III. PROPOSED SCHEME

In order to evaluate QoE metrics, we must know whethera video frame is decodable or not. The typical unit usedto evaluate the QoE metric, a video frame, exists at theapplication-layer. Due to the limited capacity of the assignedRBs in a LTE subframe, a video frame may require multiplesubframes in order to be completely transmitted to a UE. Asthe capacity of a RB is dependent on the MCS and channelconditions (channel conditions change over time), the eNBmust manage resources at the MAC-layer (RBs are assignedat the MAC layer). To perform QoE-based resource allocation,the QoE must be evaluated at the MAC layer. Thus, we proposea single round QoE utility function, which operates at the MAClayer, and an Integer Linear Programming Resource Allocationalgorithm to perform LTE subframe resource allocation.

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A. Single Round QoE Utility Function

Based on the current video information and channel con-ditions, we predict the trend of the QoE and assign resourcessuch that the predicted QoE is maximized.

In order to predict the QoE in each round we reformulatethe linear QoE utility (1) as it requires less time to calculate.For performance evaluation purposes we use the more accurateexponential QoE utility (2), which was presented in section IV.

As the original version of the utility function does notdirectly reflect the utility gain in terms of MAC-layer resourceallocation, we reformulate the linear QoE utility function (1)below.

U [i] = wre ×N

[i]buff + (1− 1

u[i] )

tnow + T× L

+wkb ×Bits[i] + S[i]

(tnow + T )(5)

where

u[i] =b[i]Fnow

S[i](6)

The wre term determines the amount of a utility’s contri-bution towards each slice of a video frame. The term 1/u[i]

captures the utility gain obtained by reducing the bufferingratio. The duration of a video frame is translated via the Lterm into the corresponding number of LTE slots; when theFrames Per Second (FPS) is 30, L is 2000. The term tnowrepresents the time tick, which is referenced with respect tothe beginning of the live video transmission.

B. Integer Linear Programming Resource Allocation

After reformulating the equation, we derive a linear systemutility U for video group [i] with respect to the resourceallocation Xji by multiplying by the group weight functiong (The group weight function g is with respect to each videogroup.)

U =maxXji

∑i

g(N [i])U [i] (7)

where

U [i] = wre ×N

[i]buff + 1

tnow + T× L+ wb ×

Bits[i]

(tnow + T )

+wre × L

tnow + T

T∑NRB

j=1 Xjim[i]j

b[i]Fnow

+ wb ×T∑NRB

j=1 Xjim[i]j

tnow + T(8)

where b[i]Fnow

is the current video frame size in bits, whichwill be transmitted next, and function g is a group weightedfunction which can be tuned by the network operator as per[7]. In section IV our simulation applies the linear version ofg, per equation (11).

Constant : g(N [i]) = 1 (9)

Logarithmic : g(N [i]) = log(N [i]) (10)

Linear : g(N [i]) = N [i] (11)

In order to formulate our problem as an Integer Linear Pro-gramming (ILP) problem, we apply the following 3 constraints(The 3 constraints reflect the features of an LTE eMBMSsystem.)

V∑i=1

Xji = 1 (12)

S[i]

b[i]Fnow

≤ 1− Bits[i]

b[i]Fnow

(13)

Xji ∈ {0, 1} ,∀i,∀j (14)

The constraints (12) and (14) limit each channel to a singlevideo at a time, these constraints are boolean variables. Theconstraint (13) places an upper-limit on the trunk size allo-cated to each video; the upper-limit prevents over-allocatingresources for packets from the content provider as the over-allocated resources would go unused.

Using the aforementioned constraints and the linear objec-tive equation (7), we use Linear Programming plus Branchand Bound to obtain the allocation pattern Xji in P-time. Asa result our solution is able to operate fast enough to be usedfor live video. We call our resource allocation method ILPResource Allocation.

In each round, after obtaining the channel conditions andthe video packet information, we use the ILP Resource Allo-cation algorithm to allocate resources. After multiple rounds,the QoE utility is used to determine how the eNB allocatesresources such that the aggregate set of users’ QoE is max-imized. The proposed algorithm is summarized in algorithm1.

IV. PERFORMANCE EVALUATION

In this section we describe our simulator, simulation set-tings, and discuss the results.

A. Enviromment

The physical-level settings of our simulation are based onthe LTE specifications [8] and [9]. The system-level settingsare based on LTE simulator [10].

The LTE simulator [10] doesn’t contain an eMBMS simu-lation. As such, we build our eMBMS simulator in MATLAB.Using our simulator we compare the performance of ourproposed resource allocation method against 3 other resourceallocation methods (The 3 other resource allocation methodsare Baseline, Throughput-Oriented, and Water-Filling). Wesummarize the simulation settings in table II.

We set our system bandwidth to 10MHz (per the LTE spec-ification [9] there are 50 effective channels when the systembandwidth is 10MHz.) We assume 40 of the 50 channels areused for MBMS purposes, while the remaining channels arereserved for unicast or other applications.

Other multicast configuration settings, such as the systempopulation, are similar to those in [7]. The video used for oursimulation, Foreman [11], is in CIF format, has a duration of300 frames, and a frame rate of 30 FPS. We encode the testvideo into a H.264/AVC video with a bitrate of 1.493 Mbps.Our system capacity and the video rate is similar to AT&T’stest scenario [12].

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Algorithm 1 Single Round ILP Resource AllocationInput: T : Allocation-Period

1: t: Time2: m

[i]j : Estimated Channel Condition

3: Ni: User in each video4: RB = NRB : Number of Carriers5: b

[i]Fnow

: Packet-Size of current video frame (remaining size)6: Bits[i]: Current amount of Received Bits7: Current Re-Buffering Times8: V : Number of Videos9: FPS: Frame Rate

Output: Channel Allocation Pattern, Xji

10: Aeq =[ ];11: L = 2000; // Slots per second12: for i = 1 to V do13: Aeq = [Aeq INRB×NRB

] //I is an identical matrix;14: end for15: beq = 1NRB×1;16: for i = 1 to V do //bi is an 4× 1 vector17: bi = b

[i]Fnow

−Bits[i];18: end for19: for i = 1 to V do20: Calculate Mapping M [i](m

[i]j )

// i.e. from MCS to BitsPerSlot;21: Aij = M [i](m

[i]j )× T ;

22: Ci = T

[wre

(t+T )b[i]

Fnow

× LFPS + wb

t+T

];

23: fji = Ni × Ci ×M [i](m[i]j );

24: end for//RunLinear-Programming + Branch-and-Bound to solve X .(E.g. ILP)//ILP maximize f s.t. constraints:AX ≤ b; Aeq = beq; Xji ∈ {0, 1};

25: Xji = ILP (f,A, b, Aeq, beq, 0, 1);26: Return Xji;

Parameter SettingSymbols per slot 6 symbols (Extended Cyclic Prefix)Number of Cells 1 Cell and see neighbor cell as interferenceCellular layout Hexagonal grid, 3 sectors/cellISD 1732m [3gpp TR36814 Table A.2.1.1-1 3GPP case 3]Central-frequency 2.0 GHzSystem bandwidth 10 MHzLocations of UE Uniform distributed in each sectorBS Power 46 dBmNumber of video streams User Defined (defaults = 4)LTE BLER upper-bound 10%N0 (AWGN Noise) Thermal noise density = -174dBm/HzPath loss and SF UMa scenario in [3gpp TR36814 Table B.1.2.1-1]CQI definition same as [3gpp TS36.213 Table 7.2.3-1]Allocation Period T 1 slot (time unit for resource allocation in table I)

TABLE II. TABLE OF SIMULATION PARAMETERS AND MODEL

B. Algorithm for Comparison

We compare our ILP resource allocation method against theBaseline, Throughput-Oriented, and Water-Filling approaches.All approaches are summarized below:

• Baseline (BL): Allocate channels in the order of thevideo group. The number of channels allocated to eachgroup are proportional to the population of group.The allocation is fixed for the entire duration of the

simulation.

• Throughput-Oriented (TO): Resources are allocatedwith the objective of maximizing throughput. In termsof QoE, only the bitrate term is considered.

• Water-Filling (WF): This method refers to the re-source allocation used in [13]. The demand for thevideo is divided by the weight (population) as thepriority. In order to make a fair comparison with ourmethod, we set the demand equal to the packet sizedivided by channel condition. In other words, a videowith small packet sizes, good channel conditions,or with a large group will have a higher priority.Resources are assigned to the videos in the order oftheir respective priority.

• ILP Resource Allocation (ILP): Resources are allo-cated according to the users’ channel conditions andthe video packet sizes. The resources are assigned suchthat the QoE utility, which is with respect to both thebitrate and the buffering ratio, is maximized.

C. Simulation Result

The performance metrics in our simulation are:

• Utility: The expected user engagement per user isevaluated per equation (2) for more accuracy and tohelp ensure that the distortion due to the linear QoEdoes not adversely effect the results.

• Buffering ratio: The number of Re-Buffering eventsdivided over the entire video length.

• Bitrate: The average bitrate (kbps) per user.

In figures 3a-3c, the aforementioned 3 metrics all improvewhen the number of available channels increase. In all cases,baseline has the worst metrics, while TO and WF have thesecond best set of metrics. WF metrics improve over TOmetrics as the number of available channels increase, this isdue to the fact that WF considers the demand of the video andthe channel while TO does not. WF only assigns resources tothe video with the highest priority, yet ILP always efficientlyassigns resources to all videos be determining the allocationwhich maximizes the QoE utility.

We show the other scenarios, fixing the number of videosand number of channels yet varying the group size, in figures3d-3f. The 3 metrics all degrade when the population of eachgroup increases, as it becomes more likely that at least one userhas poor channel conditions. To serve a user with poor channelconditions a slow MCS must be used, as determined perequation (4), i.e. the channel efficiency decreases. Regardlessof the group size, ILP always obtains the maximum QoE as itallocates resources more efficiently.

V. CONCLUSION

An on-demand resource allocation method is a necessitydue to the high volume of video traffic in LTE networks.A QoE-oriented resource allocation algorithm achieves higheruser satisfaction than a traditional throughput-oriented resourceallocation method.

In this paper we proposed a QoE-Based resource allocationmethod which efficiently allocates resources based on both thedemand of video and the channel conditions. Our algorithmmaximizes the QoE utility over the aggregate set of all users.

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30 35 40 45 500

1

2

3

4

5

6

7

Utility (VideoNum=4,GroupSize=4)

Channel num

Util

ity(s

ec)

BLTOWFILP

(a) Utility v.s. ChannelNum

30 35 40 45 500

0.1

0.2

0.3

0.4

0.5

0.6

ReBufferRatio (VideoNum=4,GroupSize=4)

Channel num

Buffe

rRat

io

BLTOWFILP

(b) BufferRatio v.s. ChannelNum

30 35 40 45 500

200

400

600

800

1000

1200

Avg.BitRate (VideoNum=4,GroupSize=4)

Channel num

AvgB

itRat

e(kb

ps)

BLTOWFILP

(c) Avg. Bitrate v.s. ChannelNum

2 4 6 8 100

1

2

3

4

5

6

7

8

9Utility (VideoNum=4,CHNum=40)

Population Per Group

Util

ity(s

ec)

BLTOWFILP

(d) Utility v.s. Population per Group

2 4 6 8 100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

ReBufferRatio (VideoNum=4,CHNum=40)

Population Per Group

Buffe

rRat

io

BLTOWFILP

(e) BufferRatio v.s. Population per Group

2 4 6 8 100

200

400

600

800

1000

1200

1400

Avg.BitRate (VideoNum=4,CHNum=40)

Population Per GroupAv

gBitR

ate(

kbps

)

BLTOWFILP

(f) Avg. Bitrate v.s. Population per Group

Fig. 3. Performance Metrics for Different Scenario

We built an LTE eMBMS simulator whose environment isbased on the LTE specifications and whose system capacityis set per AT&T’s settings. We evaluated the performance ofour ILP resource allocation method against 3 other resourceallocation methods. Our ILP resource allocation method al-ways achieves the highest QoE utility regardless of whetherthe resources are sufficient or not.

ACKNOWLEDGMENT

Hung-Yu Wei was supported in part by the Ministry ofScience and Technology of the Republic of China under Grants102-2221-E-002-077-MY and 103-2221-E-002-086-MY3.

REFERENCES

[1] R. Afolabi et al., “Multicast Scheduling and Resource AllocationAlgorithms for OFDMA-Based Systems: A Survey,” IEEE Commun.Surveys Tuts., vol. 15, no. 1, pp. pages 240–254, First 2013.

[2] F. Dobrian et al., “Understanding the Impact of Video Quality onUser Engagement,” in Proc. of the ACM SIGCOMM 2011 Conf., ser.SIGCOMM ’11. New York, NY, USA: ACM, 2011, pp. 362–373.[Online]. Available: http://doi.acm.org/10.1145/2018436.2018478

[3] X. Liu et al., “A Case for a Coordinated Internet VideoControl Plane,” in Proc. of the ACM SIGCOMM 2012 Conf. onApplications, Technologies, Architectures, and Protocols for ComputerCommunication, ser. SIGCOMM ’12. New York, NY, USA: ACM,2012, pp. 359–370. [Online]. Available: http://doi.acm.org/10.1145/2342356.2342431

[4] W. Kuo et al., “A QoE-Based Link Adaptation Scheme for H.264/SVCVideo Multicast Over IEEE 802.11,” IEEE Trans. Circuits Syst. VideoTechnol., vol. 25, no. 5, pp. pages 812–826, May 2015.

[5] 3GPP, “3GPP TR 26.849 V12.0.0. MBMS operation on Demand;MBMS Improvements, Technical Report,” 3rd Generation PartnershipProject (3GPP), Tech. Rep., December 2014.

[6] S. Lu et al., “Channel-Aware Frequency Domain Packet Schedulingfor MBMS in LTE,” in Vehicular Technology Conf., 2009. VTC Spring2009. IEEE 69th, April 2009, pp. 1–5.

[7] J. Chen et al., “Fair and Optimal Resource Allocation for LTE Multicast(eMBMS): Group Partitioning and Dynamics,” in 2015 Proc. IEEEINFOCOM, April 2015.

[8] 3GPP, “3GPP TS 36.213 V12.5.0. Physical layer procedures; EvolvedUniversal Terrestrial Radio Access, Technical Specification,” 3rd Gen-eration Partnership Project (3GPP), Tech. Rep., March 2015.

[9] ——, “3GPP TR 36.814 V9.0.0. Further advancements for E-UTRAphysical layer aspects; Evolved Universal Terrestrial Radio Access,Technical Report,” 3rd Generation Partnership Project (3GPP), Tech.Rep., March 2010.

[10] J. C. Ikuno et al., “System level simulation of LTE network,” in Proc.2010 IEEE 71st Vehicular Technology Conf., Taipei, Taiwan, May 2010.

[11] (2005) SVC test sequences. [Online]. Available: ftp://ftp.tnt.uni-hannover.de/pub/svc/testsequences/

[12] S. J. Crowley. (2011, April) The Challenge of HD Video Streamingon LTE. [Online]. Available: http://stevencrowley.com/2011/04/22/streaming-hd-video-on-mobile-broadband/#more-1977

[13] C. Ko et al., “Strategy-Proof Resource Allocation Mechanism for Multi-Flow Wireless Multicast,” IEEE Trans. Wireless Commun., pp. 1–14,2015.

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