286IEICE TRANS. COMMUN., VOL.E104–B, NO.3 MARCH 2021

PAPERQoE-Aware Stable Adaptive Video Streaming UsingProportional-Derivative Controller for MPEG-DASH∗

Ryuta SAKAMOTO†a), Student Member, Takahiro SHOBUDANI†, Nonmember,Ryosuke HOTCHI†, Student Member, and Ryogo KUBO†, Member

SUMMARY In video distribution services such as video streaming, theproviders must satisfy the various quality demands of the users. One ofthe human-centric indexes used to assess video quality is the quality of ex-perience (QoE). In video streaming, the video bitrate, video freezing time,and video bitrate switching are significant determiners of QoE. To providehigh-quality video streaming services, adaptive streaming using theMovingPicture Experts Group dynamic adaptive streaming over Hypertext TransferProtocol (MPEG-DASH) is widely utilized. One of the conventional bitrateselection methods for MPEG-DASH selects the bitrate such that the amountof buffered data in the playback buffer, i.e., the playback buffer level, can bemaintained at a constant value. This method can avoid buffer overflow andvideo freezing based on feedback control; however, this method induceshigh-frequency video bitrate switching, which can degrade QoE. To over-come this issue, this paper proposes a bitrate selectionmethod in an adaptivevideo steaming for MPEG-DASH to improve the QoE by minimizing thebitrate fluctuation. To this end, the proposed method does not change thebitrate if the playback buffer level is not around its upper or lower limit,corresponding to the full or empty state of the playback buffer, respectively.In particular, to avoid buffer overflow and video freezing, the proposedmethod selects the bitrate based on proportional-derivative (PD) control tomaintain the playback buffer level at a target level, which corresponds to anupper or lower threshold of the playback buffer level. Simulations confirmthat, the proposed method offers better QoE than the conventional methodfor users with various preferences.key words: MPEG-DASH, QoE, adaptive streaming, feedback control

1. Introduction

Internet traffic is expected to increase annually, from 77 EBin 2017 to approximately 220 EB by 2022 [1]. One of thecauses for this surge in the Internet traffic is the increasingdemand for video distribution services such as video stream-ing. Internet video viewing, for instance, in the form of videostreaming or web-based videomonitoring, is estimated to ac-count for approximately 82% of the Internet traffic in 2022.With this rapid increase in Internet traffic, it is challengingto address the traffic demand on previously unproblematiccommunication networks, and such a state may lead to theproblems of network congestion and varying network condi-tions [2]. Therefore, service providers must determine ways

Manuscript received March 15, 2020.Manuscript revised July 22, 2020.Manuscript publicized September 24, 2020.†The authors are with the Department of Electronics and

Electrical Engineering, Keio University, Yokohama-shi, 223-8522Japan.∗This paper was presented in part at the 2019 International

Symposium on Nonlinear Theory and its Applications (NOLTA2019), Kuala Lumpur, Malaysia, December 2–6, 2019.

a) E-mail: sakamoto.ryuta@kbl.elec.keio.ac.jpDOI: 10.1587/transcom.2020EBP3038

to provide high-quality video services that can satisfy therequests of the users in a limited bandwidth.

In particular, maintaining the quality of service (QoS)is important to ensure high video quality [3], [4]. Chen etal. [5] proposed a video streaming method based on QoSparameters such as the packet loss rate and transmissiondelay. However, because the QoS is an evaluation index thatdoes not consider the user-side quality, the user satisfactionmay be low even if the QoS is high. Therefore, the index ofquality of experience (QoE), which indicates the user-sidequality, has attracted considerable research attention [6]–[8].To improve the user satisfaction, the service providers mustdesign the system taking into account not only the QoS butalso the QoE. Although the QoE can be quantified basedon subjective evaluation methods, it is difficult to feed theevaluation results back to the system in real time. Therefore,some researchers proposed objective evaluation methods toestimate the QoE [9], [10].

To provide high-quality video streaming services, astreaming method was proposed by the 3rd GenerationPartnership Project (3GPP) and Moving Picture ExpertsGroup (MPEG) [11], [12]. In recent years, the MPEG dy-namic adaptive streaming over Hypertext Transfer Protocol(MPEG-DASH) has been widely used in various systemssuch as HTTP live streaming [13]. In the case of videostreaming by MPEG-DASH, the servers encode the videodata by multiple bitrates. Subsequently, the encoded videodata for each bitrate are divided at evenly spaced time in-tervals. Each divided data chunk is defined as a segment.The user terminal selects the segment with an appropriatebitrate according to the latest network condition such as thethroughput. Therefore, adaptive streaming can be used toselect the appropriate bitrate from multiple bitrates whileavoiding video interruption as much as possible.

In MPEG-DASH, the bitrate selection determines thestreaming performance, and this aspect is not specified inthe standard. Primarily, two main types of bitrate selectionmethods are available, one of which is based on throughputestimation [14]–[20], and the other is based on the playbackbuffer information [21]–[26]. Liu et al. [15] proposed abitrate selection method using the network throughput esti-mated with consideration for the ratio of the playback dura-tion of the segment and the time elapsed from the instant ofrequesting a segment to the completion of the segment down-load. Jiang et al. [20] proposed a fair, efficient, and stableadaptive bitrate selection algorithm based on the throughput

Copyright © 2021 The Institute of Electronics, Information and Communication Engineers

SAKAMOTO et al.: QOE-AWARE STABLE ADAPTIVE VIDEO STREAMING USING PROPORTIONAL-DERIVATIVE CONTROLLER FOR MPEG-DASH287

estimation. However, because the network condition such asthe throughput estimated from the information obtained viathe network may not be correct, the bitrate selection methodbased on playback buffer information, which correspondsto the client-side information, was considered. Zhou et al.[23] proposed a control-theoretic approach to select an ap-propriate bitrate for video streaming over multiple contentdistribution servers. Huang et al. [24] proposed a bitrateselection method by using a feedback controller to maintainthe playable time. This method can prevent the playbackbuffer from becoming empty and select the bitrate accordingto the available bandwidth. However, the high-frequencybitrate switching by the feedback controller may result in adeteriorated user QoE.

To overcome the QoE degradation caused by bitratefluctuation in [24], this paper proposes a novel bitrate se-lection method for adaptive video streaming using MPEG-DASH. The proposedmethod is based on the framework pro-posed in [24]. The framework utilizes the playback bufferinformation, which is the amount of the buffered data in theplayback buffer, i.e., the playback buffer level. The conven-tional bitrate selection method always calculates the bitrateby using the feedback controller. On the other hand, theproposed method does not change the bitrate if the playbackbuffer level is not around its upper or lower limit, corre-sponding to the full and empty states of the playback buffer,to suppress bitrate fluctuation. In addition, to avoid bufferoverflow and video freezing, the proposed method selectsthe bitrate based on the proportional-derivative (PD) controlto maintain the playback buffer level at a target level, whichcorresponds to an upper or lower threshold of the playbackbuffer level. In this work, simulations were performed toverify the impact of the proposed method on the QoE.

The remaining paper is organized as follows. Section 2describes the QoE model used for the QoE evaluation. Sec-tion 3 introduces the playback buffer model and the conven-tional bitrate selection method based on feedback control.Section 4 proposes the bitrate selection method to suppressthe bitrate fluctuation. The simulation results are presentedin Sect. 5. Finally, the conclusions are elucidated in Sect. 6.

2. QoE Model

This section describes the QoE model for video streamingproposed in [24]. The QoE of video streaming is affectedby the following three primary factors: video bitrate, videofreezing time, and bitrate switching [20], [27]–[29]. Usingthese factors, the total QoE value of the k-th segment forvideo streaming, that is, Qk can be defined as (1)

Qk = Qkv + ηQk

f + λQks, (1)

The coefficients η and λ are user preference weights. In-creasing η and λ values represent the QoE models of userswho are sensitive to the freezing time and video bitrateswitching, respectively. In this research, this QoE modelis used for QoE evaluation.

2.1 QoE for Video Bitrate

Typically, the QoE tends to increase with an increase in thevideo bitrate. In the existing works, the QoE characteristicsregarding the video bitrate were modeled as a logarithmicfunction [28], [29]. The QoE value for the video bitrate ofthe k-th segment, Qk

v , can be expressed as (2)

Qkv = ln rk, (2)

where rk denotes the video bitrate of the k-th segment.

2.2 QoE for Freezing Time

The amount of buffered data in the playback buffer, i.e., theplayback buffer level, is expressed as the amount of playbacktime in seconds. A playback buffer level of 0 s indicatesvideo freezing, and a higher freezing time corresponds to alower user QoE. The relationship between the freezing timeand user satisfaction is known to follow the logistic function[27], and Qk

fcan thus be expressed as (3)

Qkf = −

exp(−α + βtkf )

1 + exp(−α + βtkf ), (3)

where tkfdenotes the freezing time that occurs during video

viewing for the (k − 1)-th segment. The parameters α andβ are constant values, and they were both set as 1 in thisresearch with reference to [24].

2.3 QoE for Bitrate Switching

A higher frequency of video bitrate switching correspondsto a lower user QoE [30]. In particular, the change from ahigh to low bitrate adversely affects the user QoE. The QoEvalue for bitrate switching from the (k − 1)-th segment tok-th segment, Qk

s , can be expressed as (4)

Qks = −

µ|rk − rk−1 |

rk, (4)

where the parameter µ is a constant value, which was setas 1 in this research with reference to [24]. The QoE valueQk

s changes depending on the denominator of (4) even if theabsolute value of the difference between rk−1 and rk is thesame. The change from a high to low bitrate has a greaterimpact than the change from a low to high bitrate.

The conventional bitrate selection method [24] is analgorithm that selects the highest bitrate that does not causebuffer overflow and video freezing, and it aims to improvethe QoE by optimizing the video bitrate and video freezingtime. In contrast, the purpose of this research is to improvethe bitrate switching while maintaining the video bitrate andvideo freezing time at a constant level by implementing analgorithm that can suppress the bitrate fluctuation, therebyimproving the overall QoE.

288IEICE TRANS. COMMUN., VOL.E104–B, NO.3 MARCH 2021

Fig. 1 Playback buffer model.

3. Adaptive Streaming Based on Playback Buffer

This section describes the playback buffermodel and conven-tional bitrate selection method. The playback buffer modelis widely used in the conventional methods and is also em-ployed in the proposed method. The conventional methodenables video playback with as high a bitrate as possiblewhile avoiding video freezing.

3.1 Playback Buffer

The playback buffer is placed on the user terminal at whichthe video data are temporarily buffered. Figure 1 shows theplayback buffer model proposed in [24]. In Fig. 1, k, τ,rk , ck , and bk denote the segment number, playback dura-tion time per segment, bitrate of the k-th segment, averagethroughput for downloading the k-th segment, and remainingplayback time in seconds, i.e., the buffer level at the start ofdownloading the k-th segment, respectively. The playbackbuffer can be modeled as a first-in-first-out queue [31].

In Fig. 1, τrk , which is the playback duration multipliedby the bitrate, indicates the amount of data in the k-th seg-ment. If the network throughput is ck , the time required forthe user terminal to download the k-th segment dk can beexpressed as (5)

dk =τrkck

. (5)

After downloading the k-th segment, the playback bufferlevel increases by τ and decreases by dk for the downloadingtime. Therefore, the buffer level at the start of downloadingthe (k + 1)-th segment, bk+1, is determined considering thetime required to download the k-th segment and τ. The bufferlevel at the start of downloading the (k+1)-th segment, bk+1,when the playback buffer has no upper limit can be expressedas (6)

bk+1 = bk − dk + τ. (6)

When the playback buffer has an upper limit of blim,and the buffer level reaches blim, the download can no longerbe performed. Thus, a download waiting time is incurred[31]. The additional waiting time required to download thek-th segment can be defined as ∆tk and calculated as (7)

∆tk = max{bk − dk + τ − blim, 0}. (7)

The buffer level at the start of downloading the (k + 1)-th

Fig. 2 MPEG-DASH system with conventional bitrate selection method.

segment, bk+1, can be obtained as (8)

bk+1 = max{bk − dk + τ − ∆tk, τ}. (8)

The user terminal starts downloading the (k + 1)-th segmentafter waiting for∆tk to prevent the playback buffer from over-flowing. If the buffer level becomes 0 s during the downloadof the (k + 1)-th segment, i.e., video freezing occurs, bk+1 isequal to the playback duration time per segment, τ.

The change rate of the buffer level at time t, v (t), can becalculated using (9) by assuming that τ is added graduallyduring the download by using the fluid approximation [23]:

v (t) =db(t)

dt≈

bk − bk−1tk − tk−1

=ckrk− 1 = vk (9)

In (9), vk and b(t) denote the change rate of the buffer levelat the k-th segment and the buffer level at time t. Therefore,the buffer dynamics can be expressed as (10)

bk+1 =

k∑j=1

vj (t j − t j−1). (10)

3.2 Conventional Bitrate Selection Method

Figure 2 shows an MPEG-DASH system with the conven-tional bitrate selection method, which maintains the bufferlevel at the target value by using a feedback controller [24].In Fig. 2, bf and r ′

kdenote the target buffer level and tentative

bitrate before quantization, respectively. The conventionalbitrate selection method can select the bitrate using only theplayback buffer information without estimating the networkthroughput and can thus avoid buffer overflow and videofreezing.

Here, we consider the linearization of the system atthe operating point of bf , at which the change rate of thebuffer level vk is 0. At this operating point, the followingrelationship can be derived from (9).

r f = cf (11)

In (11), r f and cf denote the selected bitrate and averagethroughput corresponding to the playback buffer level at theoperating point, bf , respectively. The linearized continuous-time model of the MPEG-DASH system with the conven-tional bitrate selection method is shown in Fig. 3. In Fig. 3,δr and δb are obtained from (12) and (13), respectively.

δr = rk − r f (12)

SAKAMOTO et al.: QOE-AWARE STABLE ADAPTIVE VIDEO STREAMING USING PROPORTIONAL-DERIVATIVE CONTROLLER FOR MPEG-DASH289

Fig. 3 Linearized continuous-time model of the MPEG-DASH systemwith conventional bitrate selection method.

δb = bk − bf (13)

The variable s denotes a Laplace operator.A proportional-integral-derivative (PID)-based ap-

proach is implemented in the feedback controller C(s) as(14) and (15)

C(s) = −r f Cpid (s), (14)

Cpid (s) = Kp +Ki

s+ Kds, (15)

where Kp , Ki , and Kd denote the proportional, integral, andderivative gains, respectively. In the discrete-time systemshown in Fig. 2, the tentative bitrate r ′

kis calculated as (16)

r ′k = C(z)(bf − bk ) + rk−1, (16)

where C(z) indicates a digital implementation of C(s). ThePID-based controller calculates r ′

kbased on the difference

between the target buffer level and bitrate. The bitrate isincreased when the current buffer level is greater than thetarget buffer level. On the other hand, the bitrate is decreasedwhen the current buffer level is less than the target bufferlevel. The relationship between the bitrate and the changerate of the buffer level was shown in (9). By adjusting thebitrate, the feedback controller tries to maintain the playbackbuffer level at the target level to avoid buffer overflow andvideo freezing. The stability in the linearized continuous-time model has been analyzed in [24].

Subsequently, r ′kis quantized to select the appropriate

bitrate. The quantization algorithm is presented as Algo-rithm 1. In Algorithm 1, ir (i = 1, 2, · · · ,m), i, m, andN denote the selectable bitrates in ascending order, encod-ing bitrate number, number of the selectable bitrates, andtotal number of segments, respectively. The tentative bi-trate r ′

kcan be an arbitrary real number; in addition, the

number of selectable bitrates m is limited. In the conven-tional method, the tentative bitrate is rounded to the nearestselectable bitrate in any case. However, this method in-duces high-frequency bitrate switching, which causes QoEdegradation. This phenomenon occurs because the feedbackcontroller tries to maintain the playback buffer level at thetarget value by adjusting the bitrate frequently in response tothe buffer level fluctuation.

4. Proposed Bitrate Selection Method

This section describes the proposed bitrate selection methodto suppress bitrate fluctuation induced by the feedback con-troller, as described in Sect. 3. Figure 4 shows the block

Algorithm 1 Quantization (conventional method)for k ⇐ 1 to N do

for i ⇐ 1 to m − 1 doif r′

k<

i r+i+1r2 then

rk ⇐ir

break;end if

end forif r′

k≥

m−1r+mr2 then

rk ⇐mr

end ifend for

Fig. 4 MPEG-DASH system with the proposed bitrate selection method.

diagram of the proposed method. The parts that are differentfrom the conventional system shown in Fig. 2 represent theblocks of the feedback controller and quantization.

4.1 Feedback Controller

A command generator in the feedback controller changesthe target buffer level at the k-th segment, bcmd,k , or stopsthe output of the tentative bitrate r ′

kaccording to bk . The

algorithm of the command generator is shown as Algorithm2. In Algorithm 2, Bth and Uth denote the lower and upperthresholds of the playback buffer level, respectively. If thebuffer level bk is lower than Bth , the target buffer level bcmd,k

is set as Bth to increase the buffer level. If the buffer level bkis higher than Uth , bcmd,k is set to Uth to decrease the bufferlevel. In these cases, the feedback controller calculates thetentative bitrate r ′

kas (17)

r ′k = C(z)(bcmd,k − bk ) + rk−1. (17)

In the proposed method, the PD controller is adopted asa feedback controller C(z) instead of the PID controller.Therefore, the feedback controller in the continuous-timemodel C(s) is expressed as (18) and (19)

C(s) = −r f Cpd (s), (18)Cpd (s) = Kp + Kds. (19)

If the buffer level bk ranges from Bth to Uth , the feedbackcontroller does not output anything and leaves the decisionof the selected bitrate up to the quantization algorithm. Asmentioned above, in the proposed bitrate selection method,the feedback controller is active only when the buffer level isaround its upper or lower limit. This means that the calcula-tion in the feedback controller is performed intermittently. Asimple time-series integration in the feedback controller may

290IEICE TRANS. COMMUN., VOL.E104–B, NO.3 MARCH 2021

Algorithm 2 Command generator in the feedback controllerfor k ⇐ 1 to N do

if bk < Bth thenbcmd,k ⇐ Bth

else if Uth < bk thenbcmd,k ⇐Uth

elser′kis not output by the feedback controller.

end ifend for

induce unstable responses. Therefore, the proposed methodeliminates the integral element from the feedback controller.

4.2 Quantization

The quantization algorithm is implemented to suppress thebitrate fluctuation, and its process flow is defined as in Al-gorithm 3. In Algorithm 3, nk and nth respectively denotethe selected encoding bitrate number of the k-th segmentand its incremental threshold to suppress the bitrate fluctu-ation, which can be a natural number of no less than two.In the proposed algorithm, the available bandwidth must beestimated to determine the bitrate rk . From (9), ck can beexpressed as (20)

ck = (vk + 1)rk . (20)

Although rk should be used to estimate the available band-width, only the value of the previous segment is known.Therefore, the available bandwidth is estimated using rk−1instead of rk as in (14). The available bandwidth estimatedusing rk−1, c′

k, can be calculated as (21)

c′k = (vk + 1)rk−1. (21)

The value of vk is obtained directly from the change in thebuffer level.

To avoid a significant degradation of theQoE, this paperproposes a method to suppress an abrupt bitrate reduction.As shown in Algorithm 3, if the buffer level bk ranges fromBth toUth , the estimated available bandwidth c′

kis calculated

using (21), and the bitrate close to c′kis selected. It is noted

that the selected encoding bitrate number can be decrementedby one at most from the previous segment to avoid excessiveQoE degradation.

If the buffer level bk is less than Bth or larger than Uth ,the bitrate is selected based on the feedback control. It isnoted that the selected encoding bitrate number can be decre-mented by nth at most from the previous segment to avoidthe excessive QoE degradation. When the playback bufferlevel bk is around its upper or lower limits, the feedbackcontroller regulates the buffer level while accepting a certainamount of bitrate fluctuation to avoid the significant QoEdegradation caused by buffer overflow and video freezing.

5. Simulation

This section explains the simulation environment considered

Algorithm 3 Quantization (proposed method)for k ⇐ 1 to N do

if Bth ≤ bk ≤ Uth thenfor i ⇐ 1 to m − 1 do

if c′k< i+1r then

if vk ≥ 0 thennk ⇐ i

elsenk ⇐ i + 1

end ifbreak;

end ifend forif c′

k≥ mr then

nk ⇐ mend ifif k > 1 then

if nk−1 − nk > 1 thennk ⇐ nk−1 − 1

end ifend if

elsefor i ⇐ 1 to m − 1 do

if r′k<

i r+i+1r2 then

nk ⇐ ibreak;

end ifend forif r′

k≥

m−1r+mr2 then

nk ⇐ mend ifif k > 1 then

if nk−1 − nk > nth thennk ⇐ nk−1 − nth

end ifend if

end ifrk ⇐

nk rend for

to verify the proposed method and presents the simulationresults.

5.1 Setup

Simulations were performed using the network simulatorns-3. To confirm whether the proposed method is effectivewhen the throughput changes abruptly, three types of simu-lations were performed involving a fixed throughput, fluctu-ating throughput, and multiple users. In the simulations,ten different bitrates (1r, 2r, 3r, 4r, 5r, 6r, 7r, 8r, 9r, 10r) =(235, 375, 560, 750, 1050, 1400, 1750, 2350, 3600, 4500) kbpswere considered, and the case of one or two video streamingusers was assumed. The parameters of the user preferenceweights were set as “general” (η = 8, λ = 5) and “sen-sitive to switching” (η = 8, λ = 10) [24]. In [24], thepreference “sensitive to freezing” was also defined. In thisresearch, however, this preference was not considered sinceno freezing event occurred as will be discussed later. Theperformance was evaluated by considering the bitrate, bitrateswitching, freezing time, and average total QoE value. Theaverage total QoE value Q̄ is defined as (22)

SAKAMOTO et al.: QOE-AWARE STABLE ADAPTIVE VIDEO STREAMING USING PROPORTIONAL-DERIVATIVE CONTROLLER FOR MPEG-DASH291

Table 1 Parameters used in the simulations.Proportional gain Kp 0.17Integral gain for the conventional method Ki 5.0 × 10−11

Derivative gain Kd 0.85Target buffer level of the conventional method b f 20 sPlayback time of each segment τ 4 sUpper limit of buffer level blim 50 sLower threshold of buffer level Bth 10 sUpper threshold of buffer level Uth 40 sThreshold to suppress bitrate fluctuation nth 2Parameters of QoE for freezing time (α, β) (1, 1)Parameter of QoE for bitrate switching µ 1

Fig. 5 Network topology.

Q̄ =1N

N∑k=1

Qk . (22)

The average total QoE value is used only for comparing theconventional and proposed methods in the same preference.Therefore, the relative QoE value is defined as the ratio ofthe average total QoE value of the proposed method to thatin the conventional method in the same preference. Therelative QoE value is used instead of average total QoE valuefor performance comparison.

Table 1 presents the other parameters used in the simu-lations. The PID gainswere designed as follows. First, a pos-sible range of Kp was found by considering the dimensionalconversion from the buffer level to bitrate. Then, the gainKp was selected so that the QoE value was maximized in thestable region. The gains Kd and Ki were manually adjustedby considering buffer fluctuations and responsiveness. Thethresholds were designed as follows. Since freezing eventhad a tendency to occur if the buffer level was less than 10 s[24], Bth was set to 1

5 blim. On the other hand, Uth was set to45 blim so that Bth and Uth took the values which were 1

5 blimaway from lower and upper buffer limits, i.e., 0 and blim,respectively. In addition, nth was adjusted so as to maximizethe QoE value by trial and error. Figure 5 shows the networktopology used in the simulations. The transmission controlprotocol (TCP) was used for the background traffic (BGT).

5.2 Fixed Throughput

The bottleneck link capacity and the number of streamingusers were set to 2Mbps and one, respectively. Figures 6(a)and 6(b) show the simulation results for the selected bitrate,as obtained using the conventional and proposed methodsfor a BGT of 600 kbps. Considering the values of the link

Fig. 6 Selected bitrate with fixed throughput.

Table 2 Bitrate switching and freezing with fixed throughput.Conventional Proposed

The number of bitrate switching 99 11Freezing time 0 s 0 s

capacity and BGT, the available bandwidth of the user wasapproximately 1400 kbps. The number of bitrate switchingand freezing time are summarized in Table 2.

As shown in Fig. 6(a), the conventional method updatedthe selected bitrate with a high frequency as the feedbackcontrol determined the control input based on the differencebetween the target value and the response value. In con-trast, as shown in Fig. 6(b), the proposed method fixed theselected bitrate by using the bitrate as close as possible tothe throughput. The proposed method suppressed the bitratefluctuation effectively compared to the conventional methodas shown in Table 2. The freezing time was 0 s for both ofconventional and proposed methods.

The relative QoE value under each user preference isshown in Fig. 7. The relative QoE value was more than 1for both of the preferences, as the proposed method couldeffectively suppress the bitrate fluctuation. In particular,the average total QoE value increased by 11% for “general”and 26% for “sensitive to switching” by using the proposedmethod. The average total QoE value for the “sensitiveto switching” preference demonstrated greater improvementthan “general” because the bitrate fluctuation was effectivelysuppressed.

5.3 Fluctuating Throughput

The bottleneck link capacity and number of streaming userswere set to 2Mbps and one, respectively. Figures 8(a) and

292IEICE TRANS. COMMUN., VOL.E104–B, NO.3 MARCH 2021

Fig. 7 Relative QoE value with fixed throughput.

Fig. 8 Selected bitrate with fluctuating throughput.

Table 3 Bitrate switching and freezing with fluctuating throughput.Conventional Proposed

The number of bitrate switching 59 13Freezing time 0 s 0 s

8(b) show the simulation results of the selected bitrate, asobtained using the conventional and proposed methods. Inparticular, the BGT was set to 1400 kbps from 0 s to 400 s,400 kbps from 400 s to 800 s, and 1400 kbps from 800 s to1200 s. From the values of the link capacity and BGT, theavailable bandwidth for the user was approximately 600 kbpsfrom0 s to 400 s, 1600 kbps from400 s to 800 s, and 600 kbpsfrom 800 s to 1200 s. The number of bitrate switching andfreezing time are summarized in Table 3. The proposedmethod suppressed the bitrate fluctuation effectively com-pared to the conventional method as is the case with fixedthroughput. The freezing time was 0 s for both of conven-tional and proposed methods.

The relative QoE value under each user preference isshown in Fig. 9. The relative QoE value was more than 1

Fig. 9 Relative QoE value with fluctuating throughput.

Fig. 10 Selected bitrate with multiple users.

for both of the preferences, as the proposed method couldeffectively suppress the bitrate fluctuation. In particular,the average total QoE value increased by 10% for “general”and 20% for “sensitive to switching” by using the proposedmethod. The average total QoE value for the “sensitiveto switching” preference demonstrated greater improvementthan “general” because the bitrate fluctuation was effectivelysuppressed.

5.4 Multiple Users

The bottleneck link capacity and number of streaming userswere set to 10Mbps and two, respectively. Figures 10(a)and 10(b) show the simulation results of the selected bitrate,as obtained using the conventional and proposed methods.The BGT was set to 5Mbps from 0 s to 500 s and 7Mbpsfrom 500 s to 1000 s. From the values of the link capacityand BGT, the available bandwidth for the two users wasapproximately 5Mbps from 0 s to 500 s and 3Mbps from500 s to 1000 s. The number of bitrate switching and freezingtime are summarized in Table 4. The proposed method

SAKAMOTO et al.: QOE-AWARE STABLE ADAPTIVE VIDEO STREAMING USING PROPORTIONAL-DERIVATIVE CONTROLLER FOR MPEG-DASH293

Table 4 Bitrate switching and freezing with multiple users.Conventional Proposed

The number of bitrate switching (user 1) 104 19The number of bitrate switching (user 2) 84 20Freezing time (user 1) 0 s 0 sFreezing time (user 2) 0 s 0 s

Fig. 11 Relative QoE value with multiple users.

suppressed the bitrate fluctuation effectively compared tothe conventional method as is the case with the previous twocases. The freezing time was 0 s for both of conventionaland proposed methods.

The relative QoE value under each user preference isshown in Fig. 11. The relative QoE value was more than 1for both of the preferences, as the proposed method couldsuppress the bitrate fluctuation. In particular, the averagetotal QoE value of user 1 increased by 26% for “general”and 70% for “sensitive to switching” by using the proposedmethod. Meanwhile, the average total QoE value of user 2increased by 27% for “general” and 63% for “sensitive toswitching” by using the proposed method. The difference inthe bandwidth occupancy occurred after the throughput fluc-tuated owing to the QoE difference between the users. Afterthe throughput fluctuated, the available bandwidth was tem-porarily unstable owing to the congestion control functionof the BGT. Therefore, the slight difference in the segmentdownload timing between the users caused large differencesin the estimated throughput value.

6. Conclusion

This paper proposed a bitrate selection method for adap-tive video streaming using MPEG-DASH to suppress thebitrate fluctuation. The proposed method selected the ap-propriate bitrate based on feedback control and estimated thethroughput calculated by using the playback buffer informa-tion. Simulation results indicated that the proposed methodcould suppress the bitrate fluctuation effectively. The pro-posed method could improve the average total QoE valueunder any given preference even if the throughput fluctuatedand multiple users were present.

Our further study includes the introduction of machinelearning technologies into the proposed bitrate selectionmethod. Recently, machine learning technologies attract in-

creasing attention in the field of QoE-aware video streaming[32]–[34]. Estimating the QoE preference of each user bymachine learning may further improve the QoE in our pro-posed method since the parameters in bitrate selection canbe designed appropriately according to the QoE preference.

Acknowledgments

This work was supported in part by JSPS KAKENHI GrantNumber 18H03236.

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Ryuta Sakamoto received his B.E. degreein electronics and electrical engineering fromKeio University, Japan, in 2019. He is currentlypursuing his M.E. degree in integrated designengineering at Keio University, Japan.

Takahiro Shobudani received his B.E. de-gree in electronics and electrical engineering andhis M.E. degree in integrated design engineeringfrom Keio University, Japan, in 2017 and 2019,respectively.

Ryosuke Hotchi received his B.E. degreein electronics and electrical engineering andhis M.E. degree in integrated design engineer-ing from Keio University, Japan, in 2016 and2018, respectively. He is currently pursuing hisPh.D. degree in integrated design engineering atKeio University, Japan.

Ryogo Kubo received his B.E. degree insystem design engineering and his M.E. andPh.D. degrees in integrated design engineeringfrom Keio University, Japan, in 2005, 2007 and2009, respectively. In 2007, he joined the NTTAccess Network Service Systems Laboratories,NTT Corporation, Japan. Since 2010, he hasbeen with Keio University, Japan, where he iscurrently an Associate Professor at the Depart-ment of Electronics and Electrical Engineering.From 2019 to 2020, he also held the position

of Honorary Research Fellow at the Department of Electronic and Elec-trical Engineering, University College London (UCL), UK. His researchinterests include system control, optical communications, networking, andcyber-physical systems. He received the Best Paper Award from the IEICECommunications Society in 2011, the IEEE International Conference onCommunications (ICC’12) Best Paper Award in 2012, the Leonard G. Abra-ham Prize from the IEEE Communications Society in 2013, and the 2018IEEE International Conference on Intelligence and Safety for Robotics (ISR’18) Best Paper Award in 2018.