Virtual queue dropping for robust realtime video over IEEE 802.11aa wireless LANs
 WenPing Lai^{1}Email author and
 EnCheng Liou^{1}
https://doi.org/10.1186/s1363801606313
© Lai and Liou. 2016
Received: 28 July 2015
Accepted: 7 May 2016
Published: 21 June 2016
Abstract
Stream classification service (SCS) is a novel concept proposed in the newly released IEEE 802.11aa standard for robust audio and video streaming, particularly for graceful degradation of streaming quality during network congestions. Based on intraaccess category prioritisation (IACP), SCS is equipped with a pair of access category (AC) queues to differentiate realtime and nonrealtime video streams, namely (AC_VI, AAC_VI), and thus introduces the need for an efficient scheduler design; similarly for audio. In this study, we propose a novel crosslayer design called virtual queue dropping (VQD) for packet scheduling between AC_VI and AAC_VI. Driven by a realtime videopacketimportance scheme, VQD adopts conditional priority weighting to enhance the priority of Ipackets from AAC_VI so that they can fairly compete with Ppackets from AC_VI. Moreover, to achieve graceful degradation during network congestions, VQD (i.e. based on priority coupling among different component queue lengths within the same physical AC queue) is adopted so that the dropping probabilities of lowerpriority video packets are not only larger but also affectedandincreased by those of higherpriority ones. Different limits of priority retires of I and Ppackets can avoid unexpected long delays, and an optimal set of retry limits has been found to minimise the performance impact due to realtime constraints. Our results show that VQD outperforms PWD and SCSCR in various performance metrics, and achieves a winwin game for the dilemma in maintaining priority for AC_VI and fairness for AAC_VI simultaneously.
Keywords
1 Introduction
Video over wireless networks has been the driving force for the advancements of wireless technologies, and the family of IEEE 802.11 protocol standards [1] has successfully dominated the wireless LAN market. However, highquality and robust wireless video transmission is still a challenging issue because the wireless media is errorprone in signal and timevarying in bandwidth.
In the literature, there exist some studies providing service differentiation among video packets, where video packets of less importance are mapped to the lowerpriority AC queues than AC_VI either statically [5] or dynamically [6–10]. Although these studies have achieved performance gains over EDCA, the traffic impact of those downward mapped video packets to the lowerpriority AC queues has not been welldiscussed. To address this, in the newly released 802.11aa2012 standard [11], two new concepts called intra access category prioritisation (IACP) and stream classification service (SCS) have thus been proposed. IACP achieves flowlevel differentiation between realtime and nonrealtime video by a doublequeue framework, namely the primary (VI) and alternative (A_VI) AC queues as shown in Fig. 1c, also referred to as AC_VI and AAC_VI, respectively, throughout this study. Based on IACP, SCS further achieves packetlevel differentiation to pursue graceful degradation of video streaming quality during congestions by introducing drop eligibility indicator (DEI) enabling differential retransmission limits for lost video packets of different importance levels. Similarly, service differentiation can also be achieved for voice at both the flow and packet levels. In IACP and SCS, voice or video traffic flows are confined to their own (primary, alternative) AC queues, and thus generate zero traffic impact to the lowerpriority AC queues.
Both IACP and SCS introduce the need of efficient video packet scheduling between the (primary, alternative) AC queues before passing a headofline video packet to the EDCA Function of video AC (VIEDCAF), which adopts the same set of wireless channel contention parameters as EDCA. In 802.11aa2012, despite incapability of packetlevel differentiation, creditbased scheduling is recommended by default. To address this, this study proposes a novel crosslayer scheduler design called virtual queue dropping (VQD) aiming at substantial performance gains.
The remainder of this article is organised as follows. Section 2 briefly identifies the scope of 802.11aa2012 and overviews the related studies of the SCSrelated scheduler design. Section 3 describes the design principles and details of the proposed VQD scheduler, followed by various performance evaluations and extensive analyses in Section 4. Finally, Section 5 concludes this article.
2 Scope of 802.11aa and related studies of SCS packet scheduling
The authors of [12] have delivered an overview on the scope of 802.11aa2012, as well as the QoS management frame service issue of 802.11ae2012 [13], both of which serve as two amendments to the IEEE 802.112012 revised version. In addition to IACP and SCS, 802.11aa2012 also covers several other advanced issues, including groupcast with reties (GCR) for reliable and scalable transmission of multicast streams [14–16], overlapping basic service sets (OBSS) management for handling the coordination problem among multiple access points (AP) caused by today’s large deployment of 802.11 LANs, and interworking with the IEEE 802.1Q stream reservation protocol (SRP) [17] for supporting endtoend SRP when one or more 802.11 links are involved in the transmission path. However, these advanced issues are out of the scope of this study. A good overview of these issues can be found in [12, 18, 19].
Open research areas related to these issues in both the two aforementioned amendments have also been pointed out in [12]; in particular, for IACP, performance analysis on the mapping of streams or individual MAC frames to either the primary and alternative AC queues are needed, and the selection of parameters for the creditbased scheduler is desired. As for SCS, it is important to decide which packets of lower coding importance should be less retried if the wireless channel capacity becomes insufficient so as to achieve graceful degradation of multimedia transmission quality. Note that the term ‘frame’ is an official name for ‘packet’ at the MAC layer after encapsulation with the MAC header. However, to avoid any possible confusion with the term ‘video frame’ which means ‘video picture’, the terms ‘packet’ and ‘video picture’ will be used instead throughout the rest of this article.
Since the objective of this study is to design an efficient video packet scheduler for both realtime and nonrealtime video streams in the scenario of SCS, the following subsections mainly focus on two related studies of SCS schedulers, and the main contributions of our proposed VQD design.
2.1 Priority weighting and droppingbased scheduling
To the best of our knowledge, our previous study [20] contributed the first article to address the performance analyses of IACP and SCS, which is one of the aforementioned open issues in [12]. In that study, a crosslayer design called priority weighting and dropping (PWD) was proposed and compared with weighted round robin (WRR) based IACP and SCS (denoted as IACPWRR and SCSWRR, respectively), where different sets of weighting parameters were chosen and analysed for IACPWRR and SCSWRR. In general, the primary AC queue, deserving higher transmission priority, needs a larger weighting parameter than the alternative AC queue, and it is difficult to achieve priority and fairness simultaneously between the (primary, alternative) queues of the same AC, assuming that realtime and nonrealtime video streams are differentiated by mapping them to the primary and alternative AC queues respectively. However, our proposed PWD design can achieve a winwin game over IACPWRR and SCSWRR in its performance gains for both realtime and nonrealtime streams. Here realtime streams are referred to as those for interactive (twoway) communication scenarios with tight endtoend delay bounds ranging from 250 ms (humaneyes perceptible) to 400 ms (humaneyes unbearable) according to the ITU regulation, while nonrealtime streams for oneway reception scenarios with only loose endtoend delay bounds defined by the jitter buffer (also known as playout buffer) on the video receiver side.
2.2 Creditbased scheduling

Whenever one of the (primary, alternative) queues for the video AC is empty, the weighting factor of the nonempty one becomes 100 % in order for its headofline video packet to be passed to VIEDCAF; otherwise, consider the following.

Either queue is allowed to transmit only if its current credit value is positive. During the transmission period of VIEDCAF, the credit value of the sending queue is decreased at a negative slope called sendSlope, while that of the idle queue is increased at a positive slope called idleSlope. Note that, by definition, AC_VI has a larger weighting factor, a steeper idleSlope and a smoother sendSlope than AAC_VI in order to get a larger chance of transmission. Note that both the credit values of the double queues remain intact when their VIEDCAF is idle so that both the double queues become idle simultaneously.

As a result, the queue with a smaller headofline packet size (S), a larger accumulated credit value (C) and a larger weighting factor (ω) will form a smaller metric (S − C)/ω and thus have a better chance to win the transmission. Note that the credit of the idle queue is accumulated by ω_{ idle } (S _{ send } − C _{ send })/ω_{send} during its idle period.
For the implemented SCSCR scheme (for performance comparison with this study), the adopted value set of (ω_{AC_VI}, ω_{AAC_VI}) is taken to be (0.9, 0.1) as a typical case which prefers much higher priority of AC_VI than AAC_VI, and thus one expects much better performance for realtime video streams while reserving some basic level of fairness to nonrealtime video streams. (1, 0) is one extreme case that behaves pretty like a variant of strict priority, with only a minimum level of fairness. (0.5, 0.5) is the other extreme that behaves almost like roundrobin, i.e. the perfect case for fairness. However, no matter which case is taken between the two extremes, SCSCR still cannot tackle the dilemma problem between priority and fairness eventually.
2.3 Major contributions of VQD

Conditional priority weighting for nonrealtime Ipackets from AAC_VI
The details of this contribution are given in Section 3.1. The novelty of this contribution lies in the fairness (and thus quality) enhancement of nonrealtime video with a minimum tradeoff in realtime video quality degradation via boosting the relative importance of nonrealtime Ipackets from AAC_VI to make them equally compete with realtime Ppackets of any importance level, not just with Ppackets of least importance as in the case of PWD.

Virtual queue dropping of Ppackets based on prioritycoupled component queue lengths
The details of this contribution can be found in Section 3.2. VQD adopts a packet dropping scheme which is flexible, probabilistic and based on prioritycoupled component queue length in order to achieve finer adaptation to congestion level changes and thus higher video transmission quality, compared to the packet dropping scheme of PWD, which is fixeddroppingthresholds, nonprobabilistic, and based on nonprioritycoupled physical queue length.

Priority retries of I and Ppackets
The details of this contribution can be found in Section 3.3. The purpose is to find an optimal combination of priority retry limits for these two packet types, with a larger limit for Ipackets due to their higher importance, so as to minimise the performance impact due to realtime constraints. This contribution is totally unique to VQD and not considered in PWD at all.
3 Proposed crosslayer design (VQD)
In this study, the proposed crosslayer design also adopts the EPL index as the importance scheme of video packets to help the design of an efficient scheduler between the (primary, alternative) queues of the video access category before passing them to VIEDCAF, where the primary queue (i.e. AC_VI) buffers realtime video packets and the alternative queue (i.e. AAC_VI) buffers nonrealtime ones. The novelty of this scheduler design is threefolded: (1) conditional priority weighting for enhancing the scheduling probability of nonrealtime Ipackets from AAC_VI, (2) virtual queue dropping of less important Ppackets for adaptation to different congestion levels based on prioritycoupled component queue lengths, and (3) priority retries of I and Ppackets for minimising the performance impact due to realtime constraints with a larger retry limit for Ipackets. The details of these are explained below with their design principles.
3.1 Conditional priority weighting for nonrealtime Ipackets from AAC_VI
As aforementioned, realtime streams are much more tightly timebounded, and thus deserve better transmission resources than nonrealtime streams. In terms of scheduling between AC_VI and AAC_VI, it is equivalent to requiring ω_{AC_VI} > ω_{AAC_VI}.
Recall from the PWD design that the concept of EPLbased priority weighting has been demonstrated to be successful, and the above inequality was slightly modified to be ω_{AC_VI }≧ ω_{AAC_VI}, where the equality holds only when nonrealtime Ipackets (from AAC_VI) encounter realtime Ppackets of least importance (from AC_VI), e.g. P_{8}packets in the case of Fig. 2a. Also recall that the importance levelling of EPL is linearly descending from the leading Ipicture to its subsequent Ppictures within a given GOP, and the values of ω_{AC_VI} are proportional to the corresponding EPL values and equally spaced among the range [0.5, 1]. Meanwhile, the normalisation condition ω_{AAC_VI} = 1 − ω_{AC_VI} constraints the values of ω_{AAC_VI} to be within [0, 0.5]. Hence, it is difficult in general for nonrealtime Ipackets to compete with any type of realtime packets, and the best chance is to get an equal weighting factor when a nonrealtime Ipacket encounters a realtime Ppacket of least importance at the headofline. Obviously, this could be the weakness of PWD because Ipackets with long delays can induce much more serious error propagation than any subsequent Ppackets within the same GOP in terms of the decoded video quality at the video receiver when exceeding the playback deadline.
Conditional priority weighting
(ω _{ AC_VI }, ω _{ AAC_VI })  Nonrealtime packets via AAC_VI  

I  P_{1} ~ P_{8}  
Realtime packets via AC_VI  I  (1, 0)  (1, 0) 
P_{1} ~ P_{8}  (0.5, 0.5)  (1 − mα, mα) 
3.2 Virtual queue dropping of Ppackets based on prioritycoupled component queue lengths
In general, dropping Ppackets of less importance can help to achieve some level of graceful degradation during network congestions. Recall that PWD adopted a fixed scheme for dropping Ppackets based on the physical queue length of AC_VI or AAC_VI, where different dropping thresholds (DTs) were set for Ppackets of different EPL indices, with a lower DT value for a less important packet and a higher DT value for a more important one. In this study, we propose a flexible scheme for dropping Ppackets of different EPL indices based on the component queue length of each individual EPL index and its coupling relationship to those of other EPL indices. We call such a scheme as virtual queue dropping (VQD).
The objective of VQD is to achieve a better graceful degradation by flexible adaptation to the variation of congestion level, and the design principle behind VQD is to form coupling relationships among the multiple component queue lengths associated with different EPL indices within the same physical AC queue, such as AC_VI or AAC_VI. More details are explained below.

an asymptotic phase of low dropping probability when q _{ phy } << F _{ i }

an asymptotic phase of high dropping probability when q _{ phy } >> F _{ i }

a transition phase where P _{ drop,i } rapidly changes from low to high or from high to low when q _{ phy } is around F _{ i } (note that P _{ drop,i } = 1/2 when q _{ phy } = F _{ i }, i.e. at the inflection point).

Let us consider a video stream of a GOP structure with a period of nine video pictures (one leading Ipicture followed by eight Ppictures) as a typical example. Since every Ipicture is referenced for decoding its subsequent Ppictures, none of Ipackets should be dropped until the congestion level exceeds the limit of physical queue (q _{ limit }).

For Ppackets, eight branches of dropping probability functions with different values of inflection points are adopted so that P_{1}packets have the largest value (F _{1}) and P_{8}packets the smallest value (F _{8}). As a result, for the same value set of κ and q _{ phy }, the dropping probability of P_{1}packets is the lowest one (P _{ drop,1}) while that of P_{8}packets is the highest one (P _{ drop,8}). Namely, the smaller value for i, the higher importance level it stands for.

Moreover, even for a given value of level i, F _{ i } is still not a fixed value. Eq. (2) shows the definition of F _{ i }, where the value of F _{ i } runs within (0, q _{ limit }) and is related to two parameters: q _{ v,i } and γ. q _{ v,i } is based on a concept called virtual queue length which is a ratio running between 0 and 1. Its definition can be found in Eq. (3), where the numerator is the sum of the component queue length of level i (denoted as q _{ i }) and those of higher importance levels (denoted as q _{ j <i }), and the denominator q _{ phy } is the total physical queue length, namely the sum of all the component queue lengths. The design goal of q _{ v,i } is to allow for a congestionadaptive adjustment of the dropping probability of level i influenced by those higher importance levels (i.e. those js < i). Note that the value of i is equivalent to (L _{ GOP } − EPL), as shown in Fig. 2a. For example, q _{ v,4} is the virtual queue length of P_{4}packets, and q _{ v,4} = (q _{4} + q _{3} + q _{2} + q _{1} + q _{0})/q _{ phy }. Hence, it is easy to see the two extreme cases: q _{ v,1} = (q _{1} + q _{0})/q _{ phy }, and q _{ v,8} =1. Note that q _{0} is the component queue length of Ipackets, q _{1} is that of P_{1}packets, and thus q _{ i } is that of P_{ i }packets accordingly. On the other hand, γ simply serves to weaken the competition capability of P_{1}packets so that they will not jeopardise Ipackets when the congestion level becomes extremely heavy. In principle, to confine F _{ i } within (0, q _{ limit }), the value of γ should also be limited within (0, 1). An optimal value set of (κ, γ) is presented and discussed in Section 4.2 and Fig. 5.
3.3 Priority retries of I and Ppackets

The priority retry limits of I and Ppackets should be controlled so that the performance impact due to realtime constraints can be as small as possible.

Furthermore, because Ipackets are more important than Ppackets within the same GOP, Ipackets should deserve a larger limit of retries than Ppackets.
A series of studies to find the optimal combination of priority retry limits for these two packet types, under various congestion cases and time constraints, are presented and analysed in Fig. 6 and Section 4.3, and those performance comparisons among different designs are also based on this optimal combination to be fair.
To gain a deeper insight, it could be helpful to see how wireless errors induce packet drops if the limit of retries is exceeded. Although wireless error sources cover receiving power attenuation, signal interference, multipath fading etc., the VQD design adopts the tworay ground reflection model for simplicity, to avoid the complexity of multipath fading so that the main focus can be placed on the multiaccess collision issue. Multiaccess collisions (including both physical collisions among wireless stations and instation virtual collisions among ACs) can trigger transmission retries in the MAC layer, and eventually packet drops if the limit of retries is exceeded in physical collisions.
Obviously, the physical collision probability enlarges with the number of wireless stations (N_{s}) or the total traffic rate before it reaches a saturated rate. On the other hand, the virtual collision probability should also enlarge with the number of ACtraffic flows and their flow rates similarly. Nevertheless, based on our study, virtual collisions only increase packet delays, but do not drop packets even if over retried, and thus it is enough to understand or predict the packet loss rate (PLR) simply from the physical collision probability, without knowing the details of the virtual collision probability. In this study, we boldly assume that the physical collision probability of 802.11e and 802.11aa should be the same as that of 802.11 if they all adopt the same value of initial contention window (W) for random backoff in the retry process, ignoring the fact that 802.11 might adopt a different value for fixed backoff. In other words, they all share the same functional form of W. The results for some tested conditions of PLR, presented in the second half of Section 4.3, verify and support this assumption. Thus, the remaining questions are (1) how to predict the physical collision probability and (2) how to predict PLR from the physical collision probability.
As also discussed in [24], the above meanvalue model is actually a special case of the twostate Markov chain model when the conditional channel access probability of the current slot immediately after an idle time slot is equal to that immediately after a busy time slot. It is because of this special case that the physical collision probability can be solved uniquely. Markov chain modelling has been useful in analysing communication or networking problems, more applications of Markov chain modelling can also be easily found elsewhere, such as semiMarkov decision process modelling in helping the handoff design of a train moving between access points so as to improve the train control system performance [25], or in achieving an optimal joint session admission control scheme in integrated WLAN(802.11e)/CDMA networks to utilise overall radio resources [26], both in a fashion of crosslayer design.
4 Results
This section presents our simulation results to demonstrate the superiority of the proposed VQD design over the existing ones in achieving a winwin game for both the realtime and nonrealtime video performances through AC_VI and AAC_VI, respectively. Firstly, we describe the simulation setup, including the networking topology and experimental settings for various traffic flow types, followed by analyses spanning over different performance metrics and influential factors, including the effects of the functional shape of tanh due to optimisation of κ and the inflection point of tanh due to optimisation of γ, the retry effects of I and Ppackets, the average queue lengths, the packet delay probability distributions and their associated real and effective losses without and with a realtime constraint respectively, and the effect of the adopted reference video sequences on the peaksignaltonoiseratio (PSNR) variation under various congestion levels.
4.1 Experimental setup
Experimental settings for various traffic types, mapped AC queues, traffic sources and input rates
Traffic type  AC queues of AP  Rate from the Internet (bps)  Rate from the wireless LAN (bps) 

Realtime audio  AC_VO  Not applied  Not applied 
Nonrealtime audio  AAC_VO  n × 40 k  Not applied 
Realtime video (conversational)  AC_VI  Not applied  512 k 
Nonrealtime video (ondemand)  AAC_VI  512 k  Not applied 
Best effort  AC_BE  n × 300 k  50 k 
Background  AC_BK  n × 300 k  50 k 
4.2 Optimal values for κ and γ

For all the applied realtime constraints, κ = 1 can generate the optimal performance.

The performance differences between κ = 1 and the other values are not so large, around 1~2 dB, and thus it indicates the appropriateness and stability of the adopted tanh function for performance robustness of the proposed VQD design.

For all the applied realtime constraints, γ = 0.9 can generate the optimal performance.

The performance gains of γ = 0.9 over the other values are much larger (particularly in comparison with γ = 0.7 and 0.8), namely more visible than the performance difference among the κ values. Hence, it implies that it is more sensitive and significant to select an optimal value of γ. Meanwhile, the performance becomes more stable and robust when γ ≥ 0.9.
Based on the above analyses, the optimal value set (k = 1, γ = 0.9) is adopted for further performance analyses throughout the rest of this article.
4.3 Effects of priority retries of I and Ppackets
The design principle for priority retries of I and Ppackets has been described and discussed in Section 3.3. Figure 6 presents a series of studies on the effects of priority retries of I and Ppackets on Average YPSNR variations of different schedulers (SCSCR, PWD, VQD) under various congestion cases, with loose and tight realtime cuts (i.e. RTCut400 ms and RTCut200 ms), respectively, where the limit of retries ranges from 0 to 4 for Ipackets and from 0 to 3 for Ppackets, requiring the limit of retires for Ipackets be always larger than that for Ppackets. A common agreement has been observed: every design reaches its best performance when one sets the limits to be two retries for Ipackets, and no retry for Ppackets, denoted as I_{2}P_{0}. For further performance studies throughout the rest of this article, I_{2}P_{0} is the default case for all the designs in order to provide fair performance comparisons.
4.4 Average queue lengths

For AC_VI, the proposed VQD design outperforms the others in the sense that it is least congested at all the congestion cases, and its superiority is amplified when n increases.

For AAC_VI, the superiority of VQD over the others is still clearly seen, but no obvious increasing trend with n is observed since the ratio of scheduling probability for AAC_VI to that for AC_VI is low in general, and thus the average queue lengths of AAC_VI are longer than those of AC_VI for all the congestion cases, even at n = 1.
To sum up, a winwin game for both realtime and nonrealtime video applications has been achieved by the proposed VQD design in terms of the shortest average queue length for each congestion case.
4.5 Packet delay probability distributions with/without a realtime constraint

In light congestion, all the packet delay distributions are mostly focused below 200 ms. However, it still clearly indicates that \( {\overline{d}}_{VQD} \) < \( {\overline{d}}_{PWD} \) < \( {\overline{d}}_{SCSCR} \), where \( \overline{\mathrm{d}} \) stands for the average packet delay.

In heavy congestion, all the packet delay distributions are spread out in different extents: SCSCR is spread out most seriously, PWD secondly seriously, and the proposed VQD least seriously in the sense that the inequality \( {\overline{d}}_{VQD} \) < \( {\overline{d}}_{PWD} \) < \( {\overline{d}}_{SCSCR} \) still holds.
(r _{ RS } _{,} r _{ RC }, r _{ R }) of AC_VI versus congestion case n, due to RTCut200 ms, with the leading values from the target designs written in boldface
r _{ RS } r _{ RC } r _{ R }  Congestion case  

n = 1  n = 2  n = 3  n = 4  n = 5  n = 6  
VQD  80 %  70 %  52 %  42 %  29 %  15 % 
4 %  9 %  16 %  23 %  31 %  41 %  
84 %  79 %  68 %  65 %  60 %  56 %  
ᅟ  
PWD  79 %  68 %  50 %  27 %  12 %  5 % 
4 %  12 %  25 %  43 %  54 %  56 %  
83 %  80 %  75 %  70 %  66 %  61 %  
ᅟ  
SCSCR  77 %  65 %  30 %  20 %  9 %  7 % 
4 %  15 %  50 %  55 %  59 %  57 %  
81 %  80 %  80 %  75 %  68 %  64 % 

Although the proposed VQD design does not always have the maximum value in r _{ R } (in fact, it always takes the final place in r _{ R } except for n = 1 due to its effective packet dropping behaviour, particularly when the congestion level increases), it does always take the lead in both r _{ RS } and r _{ RC } for all the congestion levels, meaning that it keeps the minimum value in average packet delay no matter how the congestion level changes, as aforementioned.

SCSCR is at the other extreme; although it in general takes the lead in r _{ R }, it is almost the alltime loser in both r _{ RS } and r _{ RC }. This originates from the fact that SCSCR lacks an effective strategy in packet dropping to mitigate the congestion.

The performance of PWD is in general in between VQD and SCSCR; its performance is closer to VQD from light to mediumlow congestions (n = 1, 2, 3), but closer to SCSCR from mediumhigh to heavy congestions (n = 4, 5, 6).
Ratio of received packets (r _{ R }) of AAC_VI versus congestion case n, where the real packet loss rate due to transmission can be derived from (1−r _{ R }), with the leading values from the target designs written in boldface
r _{ R }  Congestion case  

n = 1  n = 2  n = 3  n = 4  n = 5  n = 6  
VQD  34 %  29 %  26 %  21 %  18 %  16 % 
PWD  31 %  25 %  21 %  17 %  15 %  13 % 
SCSCR  35 %  28 %  23 %  21 %  20 %  19 % 

VQD outperforms PWD by a superior realisation of prioritised early packet dropping which is based on the coupling of video packet dropping probability for a given priority with those higher priorities, as shown in Eq. (3).

VQD can effectively alleviate the congestion level by early dropping video packets of less importance (i.e. those lowerpriority ones) at congestion cases n = 2, 3 and 4.

Although VQD is not as good as SCSCR in the other cases in terms of r _{ R }, Fig. 10 demonstrates that VQD is still the alltime winner in terms of Average YPSNR. Again, this supports that VQD can achieve a winwin game in both realtime and nonrealtime video applications, namely it successfully tackles the dilemma problem in achieving priority and fairness simultaneously. Section 4.6 will address more on the generality of such a winwin game.
4.6 Generality of performance superiority in average YPSNR

The performance superiority of VQD over PWD and SCSCR is indeed a general phenomenon, and there is no strong effect due to the format or motion level of the adopted video sequence.

VQD can always achieve a winwin game over the others in both the performances of AAC_VI (without any realtime constraint) and AC_VI (with different realtime constraints), and its realtime performance gains over the others are amplified in general as the congestion level increases.

Although the congestionrobustness capabilities of different designs all go down with the tightness of the adopted realtime constraint as expected, the performance gains of VQD over PWD (the alltime second place) also become more visible. This shows the true power of VQD in its efficiency of early packet dropping based the coupling of component queue lengths of different priorities.
5 Conclusions
In this article, we have presented a novel scheduler design called VQD for robust realtime video communication over IEEE 802.11aa wireless LANs. Based on the three novel concepts, namely conditional priority weighting for nonrealtime Ipackets from AAC_VI, virtual queue dropping of Ppackets based on prioritycoupled component queue lengths and priority retries of I and Ppackets, the proposed VQD design has been demonstrated to be in general superior to other target designs such as SCSCR and PWD. The optimal value of κ that determines the best tanh functional shape, namely the virtual queuebased packet dropping probability functions for AC_VI and AAC_VI, has been found to be 1, despite that the performance differences using its neighbouring values are not so large, between 1~2 dB in general. The retry effect for lost I and Ppackets indicates that the best retry limits are twice for Ipackets, zero for Ppackets. This result is common to all the target designs, and thus all the performance comparisons are based on such a combination of retry limits.
VQD has revealed its superiority over PWD and SCSCR in the sense that it has the shortest average queue lengths for both AC_VI and AAC_VI, the minimum average video packet delay, the maximum number of received and survived video packets, and the highest value of average YPSNR, considering their variations with different congestion levels under various realtime constraints. Moreover, the superiority of VQD is a common phenomenon among different video sequences, and not obviously affected by the format or motion level of the adopted video source. It is also worth mentioning that VQD has won a winwin game over the other designs in both the performance gains of AC_VI and AAC_VI, namely a dilemma in achieving priority and fairness simultaneously for scheduling between the primary and alternative queues in the access category of video for delivering video streams with and without realtime constraints, respectively.
Declarations
Acknowledgements
This study was supported by the Taiwan National Ministry of Science and Technology under grants MOST 1022221E155007, MOST 1032221E155052, MOST 1042218E155002 and MOST 1052218E155001.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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