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Aggregated QoS Mapping Framework for Relative Service Differentiation-Aware Video Streaming Jitae Shin, Jin-Gyeong Kim, JongWon Kim, D. C. Lee, and C.-C. Jay Kuo Integrated Media Systems Center and Department of Electrical Engineering-Systems University of Southern California, 3740 McClintock Ave. EEB435, Los Angeles, California 90089-2564 E-mail: jitaeshi,jingyeok, jongwon, cckuo @sipi.usc.edu,
[email protected]
f
g
Abstract This research investigates the Quality of Service (QoS) interaction at the edge of differentiated service (DiffServ) domain, denoted by video gateway (VG). VG is responsible for coordinating the QoS mapping between video applications and DiffServ enabled network. To accomplish the goal of achieving economical and high-quality end-to-end video streaming, which utilizes its awareness of relative service differentiation, the proposed QoS control framework includes the following three components: 1) the relative priority based indexing and categorization of streaming video content at sender, 2) the differentiated QoS levels with load variation in DiffServ networks, and 3) the feedforward and feedback mechanisms assisting QoS mapping of categorized index to DS level at the proposed VG. Especially, we focus on building a framework for dynamic QoS mapping, which intends to overcome both the QoS demand variations of CM applications (e.g., varying priorities from aggregated/categorized packets) and the QoS supply variations of DiffServ network (e.g., varying loss/delay due to fluctuating network loads). Thus, with the proposed QoS controls in both feedforward and feedback fashion, enhanced quality provisioning for CM applications (especially video streaming) is investigated under the given pricing model (e.g., DS level differentiated price/packet). Keywords: Differentiated services (DiffServ), quality of service (QoS), relative service differentiation, relative priority index (RPI), pricing, and streaming video.
I. I NTRODUCTION Internet applications have very diverse requirements on the network service, thus making the current best-effort (BE) Internet model less than sufficient. The emerging continuous media (CM) application demands more stringent QoS requirements than traditional TCP-based applications. Under the BE model, maintaining the end-to-end CM quality is too challenging due to the fact that the CM stream is inherently variable bit rate (VBR) and that the Internet is an unpredictable time-varying channel. The emerging differentiated service (DiffServ or DS) [1] scheme in IP-QoS methods can provide QoS service in a simple and scalable manner, while integrated service with RSVP signaling protocol provides per-flow based end-to-end QoS but scalability problem. On-going research efforts in DiffServ can be divided into absolute differentiation [2, 3] and relative differentiation [4, 5]. The absolute service differentiation seeks to guarantee the QoS for a set of aggregated flows regardless background traffics. With the absolute service differentiation, per-flow QoS is typically achieved through admission control. The relative service differentiation [4,5] attempts to maintain the quality gain or loss, which results from the dynamic nature of input traffic, proportional among DS levels (e.g., behavior aggregates [1]). With a trend that Internet adapts scalable network QoS schemes and networked CM applications become more network-aware and adaptive, the relative service differentiation may be more attractive due to its simplicity and flexibility. Under the relative service differentiation paradigm, this paper presents a framework in which streaming video senders, video receivers, and a special boundary node (which we refer to a video gateway (VG) located at the boundary of DS domain without requiring per-flow information at the nodes inside the DS domain) interact with one another to control the video streaming QoS under a cost constraint. In this framework, the video application at the source should grade the chunks (i.e., packets) of its content by a certain indices according to their importance for the application-layer QoS (e.g., impact of the packet loss or delay on the Video quality at the receiver). Since these indices reflect the desired service preference of one portion with respect to other portions, we denote it as relative priority index (RPI), which is further divided into relative loss index (RLI) and relative delay index (RDI), respectively. Then, the QoS control takes place in the form of assigning each packet with an appropriate DS level, which we call QoS mapping. The VG, as a special case of media gateway, is responsible for this QoS mapping at the ingress of a DiffServ domain and for managing traffic according to the traffic conditioning agreement (TCA) specified in the service level agreement (SLA) with this DiffServ domain. To accomplish the goal of achieving economical and high-quality end-to-end video streaming, which utilizes its awareness of relative service differentiation, the proposed QoS control framework includes the following three components: 1) the relative priority based indexing and categorization of streaming video content at sender, 2) the differentiated QoS levels in DiffServ networks, and 3) the feedforward and feedback QoS mapping of categorized index to DS level at the proposed VG. Especially, we focus on building framework for dynamic QoS mapping to overcome both the QoS demand
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variations of CM applications (e.g., varying priorities from aggregated/categorized packets) and the QoS supply variations of DiffServ network (e.g., varying loss/delay due to fluctuating network loads). Thus, with the proposed QoS controls in both feedforward and feedback fashion, enhanced quality provisioning for CM applications (especially video streaming) is investigated under a pricing model (e.g., DS level differentiated price/packet). The rest of the paper is structured as follows. In Section II, we brief our proposed QoS mapping framework with VG for QoS control. Section III describes QoS mapping problem formulation in detail and gives a mapping guidance from ideal situation. Section IV presents dynamic QoS mapping algorithms and required traffic conditioning component. Various sets of performance evaluation through simulation are presented in Section V. Finally Section VI concludes this paper. II. P ROPOSED Q O S M APPING A RCHITECTURE The architecture that we propose is illustrated in Fig. 1. Sources (end system storing the video contents) within the customer network (customer of the DS domain) send video to the video clients. The traffic leaves the customer network through Video Gateway (VG). The customer network subscribes to the DS services, and the traffic is delivered to the clients through the DS domain. The customer network has SLA (service level agreements) [1] with the DS domain. Each CM application will demand its loss rate/delay preference by marking its DS field based on RPI, which comprises both RLI and RDI. That is, CM packets are categorized by RLI/RDI and the DS byte (i.e., DiffServ codepoint: DSCP) is marked accordingly. With this, we are supporting the packet-based differentiation granularity. We proposed RLI association based on the corruption model [6] to estimate the loss impact of a packet. The corruption model is a tool used to estimate the impact of a macroblock’s loss on the overall received video quality. This impact can be measured by the induced distortion (e.g., in terms of mean square error, MSE). For a motion-compensated video coder (in our case ITU-T H.263+), we can model the MSE increase caused by the loss of a macroblock (MB) by taking into account error concealment, temporal dependency (by motion vector and coding mode), and loop filtering. The source categorizes video packets into classes, each of which is represented by an integer k . The class (category) can be simply be interpreted as the discretization of RPIs. The source furnishes each packet with this categorization information (value of k ) for the VG when it sends the packet stream to the VG. The VG forwards the packet streams to their clients through the DS Domain. Thus, the video streams from the sources merge in the VG. The main function of the VG is to assign to packets the DSCPs and to dynamically schedule their transmission into the DS domain on the basis of the packets’ category (k number) and the traffic condition. This assignment of codepoints can be viewed as the mapping from category (k ) to DS behavior aggregate, which we represent by another integer variable q and we call it as class-based differentiation granularity. (We will use the phrases behavior aggregate and DS-level interchangibly.) The VG must observe the traffic conditioning agreement (TCA) with the DS-domain, so the goal of the VG is to achieve the best video qualities at the client side within the constraint of the TCA. Unlike the sources, the VG can observe the traffic behavior of the all the video streams going through it. Thus, the VG can assign the DS level to the packets from the perspective of corporate quality of video streams. In order to share the DS services fairs among the sources and protect resources from a selfish source, VG exercises the traffic shaping on a per-flow basis through the Token Buckets (TBs) assigned for individual flows (sessions), as seen in Fig. 1 (There is an agreement between the source and the VG regarding the allowable traffic behavior for each session.). The packets violating this agreement (enforced by the token bucket) are assigned with the lowest DS level (best-effort grade), which we call session-based differentiation granularity. For the packets conforming to the token bucket, the VG Performs the k to q mapping (DS marking) for each flow individually on the basis of quality optimization, which will be discussed in section III more in detail. Then, the flows whose packets are marked with DS codepoints merge at the traffic management (TM) module. The TM module adjust the codepoints on the basis of dynamically varying traffic load in order to satisfy the TCA. The function of the traffic management module will be discussed in section IV-A.2 more in detail. Under this environment, we are focusing on the dynamic and aggregate QoS mapping control at the VG for CM applications. As shown in Fig. 1, one can think of two kinds of mechanisms: feedforward and feedback. With feedforward QoS control, which is the default mode of operation covering mentioned all three-level granularity, we treat effective QoS mapping between aggregate categorized CM packets and network DS levels under the SLA. More detailed algorithm on feedforward QoS mapping will be covered in Section IV-A. The complementary feedback QoS control assumes the feedback support from end-systems covering session-based granularity only. By adjusting the mapping categorization of RPI into network level (i.e., k ! q ) upon the receiver’s feedback report, the quality of streaming video can be better
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FEEDBACK CONTROL (change indication for k → q)
RPI from application’s priority then RPI → k
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Legend RPI : relative priority index IN : interior node SLA: service level agreement TB: token bucket TM: TB-based marker
Fig. 1. Overall diagram for feedforward and feedback QoS mapping control.
managed. It provides dynamic QoS mapping adjustment between end-systems that are connected as fine-grain. More detailed algorithm is described in Section IV-B. III. Q O S M APPING P ROBLEM F ORMULATION AND G UIDANCE S OLUTION In the proposed framework, matching RPI of a video packet with loss/delay differentiation of the DiffServ network is the key to its success. We first formulate the interaction framework into the following QoS mapping problem. Then, we are deriving the solution for ideal case, to utilize it as a guidance to our dynamic QoS mapping of Section IV. A. QoS Mapping Problem Formulation
el a I(D D R
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Fig. 2. Overall QoS mapping control for categorized video packet in DiffServ network.
The overall QoS mapping interaction is illustrated in Fig. 2. As shown in the source side, the request of two example video applications (A) and (B) have two different constant RDIs and have additional variation per packet based on RLI. In the proposed QoS mapping control, if the DS domain provides the Assured Forwarding (AF) services, RDI may be used to decide the AF class queue that can match with requested delay range (This is under the assumption that the DS domain differentiates the AF classes in terms of delay performance.). RLI then may be used to decide the drop preference in each class-queue or chooses one drop DS-level among several class-queues. Thus, for the sake of simplicity, we currently enforce constraint based on RDI. That is, for each flow f , the RDI (f ) (i.e., RDI is fixed as discussed) limits the allowed range of [qmin ; qmax ] to which category k packet can be mapped without violating the delay requirement. Also, we want this mapping ordered such that packet with higher k will only be mapped to q at least equal or larger than that of lower k packet.
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Then, the problem only needs to address the RLI-based optimization as follows. Each packet i of flow f assigned to a certain network DS level q will get a average packet loss-rate lq by paying unit price p q . Given the acceptable total cost P (f ) , the effort to achieve the best end-to-end quality for flow f can be formulated by minimizing the generalized quality degradation QD (f ) ,
0N f X = min @
( )
min QD(f ) q~i
q~i
i=1
RLIi(f )
1 lq i A
X
N (f )
subject to
( )
i=1
pq(i) P (f )
(1)
where total packet numbers of flow f is N (f ) , a QoS mapping is denoted by q~i = fq (1); q (2); : : : ; q (N (f ) )g, and q (i) is DS level to which i th packet is mapped. If we fix the mapping decision for all packets belongs to category k , Eq. (1) becomes simplified to Eq. (2). Note that the QD in Eq. (2) is a expected QD, where the loss effect of a packet belonging to category k is represented by the average loss effect RLIk .
min QD ~qk
(f )
= min ~qk
X
K
1
k=0
(f )
RLIk
lq k nk ( )
(f )
!
X
K
subject to
1
k=0
X
K
pq(k) n(kf ) P (f )
for
1
k=0
n(kf ) = N (f ) :
(2)
where nk is the packet number of k category for flow f and a QoS mapping is denoted by q~k = fq (0); q (2); : : : ; q (K 1)g. Note that in real situation we have to limit the resource allocation based on the traffic conditioning agreement specified in the SLA with the DiffServ domain. Thus, the above formulation has to be constrained further by the traffic conditioning agreement in order to be utilized. (f )
B. Per-flow QoS mapping Guidance in Ideal Case To use as the guidance solution, optimal solution for idealized situation is derived here, assuming that the network DS levels maintain consistent and proportional differentiation. Note that with some proprietary well-configured scheduling [4, 5] or adaptive weighted fair queueing [7], delay can be proportionally differentiated according to DS level, albeit with some fluctuations. Thus, for this idealized network situation, the packet loss-rate l q should decrease exactly as DS level q increases. Typically, as shown in Fig. 3(b), loss-rate l q may increase inversely proportional to DS level q increases 1 . Unit price pq is then assumed to be proportional to DS level q , too. 50 45 40 35 30 25 20 15 10 5 0
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Fig. 3. (a) Several RLI distribution patterns to characterize categorized RLI distributions, (b) Typical packet loss-rate ql and unit cost pq relationship w.r.t. DS level q , (c)Numerical solutions for effective QoS mappings for different RLI source distributions.
The optimal (or effective) mapping solution q~i = fq (1); q (2); : : : ; q (N (f ) )g to Eq. (1) can serve as a guidance. This constrained optimization problem can be solved by finding the QOS mapping q~i that minimizes each Lagrangian cost
Ji () = RLIi lq (i) + pq(i) :
(3)
1 Note that the loss-rate and DS level will take diverse relationship in real practice. However, this typical relationship seems to provide a reasonable approximation.
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Since we have removed the total cost constraint of Eq. (1), for a given operating , the minimum can be computed independently for each packet with additivity. The point on the QD (f ) vs. P (f ) characteristic that minimizes J i () is the point at which the line of absolute slope is tangent to the convex hull of the QD vs. cost characteristic. Further assuming knowledge on the video source RLI distribution, one can finally get a closed-form solution for Eq. (1). For this, we have approximated RLI distribution of sequences shown in Fig. 3(a) as C 1 U (i)2 for ‘Akiyo’, C 2 U (i) for p ‘Hall’, and C3 U (i) for ‘Stefan’ and ‘Foreman’ sequence when sorted with RLI increasing orders. With loss-rate function lq(i) = L=q (i) and unit price function P l q (i)(as shown in Fig. 3(c)), where C 1 ; C2 ; C3 ; L, and Pl are given constants, the Lagrangian cost of Eq. (3) becomes
Ji () = C2 U (i)
L + (Pl q (i)): q (i)
(4)
for the case of RLI pattern using C 2 U (i). Then, by searching around the convex hull from the graph of QD vs. cost, i () we can get the optimal mapping solution. Also, we can even get a closed-form solution using @J @q(i) and the constraint
Pp
with equal signpin Eq. (1). p The resulting closed form solution is expressed by = C 2 Lp Pl =P 2 [ i U (i)]2 and P 2 q (i) = P=Pl U (i)= i U (i). If RLIi has other distribution patterns like C 1 U (i) , C3 U (i), then an optimal QoS p mapping is the forms that are proportional to U (i) and 4 U (i), respectively. This closed form solution actually matches with the general pattern of actual numeric solutions depicted in Fig. 3(c) for ‘Akiyo’, ‘Hall’, ‘Stefan’, p respectively. Note that the RLI distributions in Fig. 3(a) are approximated by patterns of 2 C1 U (i) , C2 U (i), and C3 U (i), respectively. IV. DYNAMIC Q O S M APPING M ECHANISM FOR AGGREGATED F LOWS Based on the per-flow QoS mapping guidance in Section III-B, we move on the dynamic case (i.e., time-varying network service quality) for aggregate flows. In real situation, the proposed QoS mapping should incorporate additional issues that cannot be assumed. First, in general, the whole distribution of RPI is not available at a starting time for QoS mapping and thus VG should try to do the best mapping of a flow just with the known observed RPI’s or categorized RPI’s. Next, we cannot assume that the DS domain can provision the ideal proportional differentiated service all time interval. Another implication that real situation brings in is that we need to limit the resource allocation based on the traffic conditioning agreement specified in the SLA with the DiffServ domain. Thus, in order to address these limitations, the dynamic QoS mapping mechanism is required beyond the mapping guidelines discussed in section III. A. Dynamic feedforward QoS mapping control A.1 QoS Mapping control
Sending end-system 1. Assign RLIi to each packet i based on video-content. 2. Assign RDI (f ) to a flow f according to requested delay range. /* RDI(f ) restrict the mapped q range */ 3. Categorize RLIi into k categories by discretizing packets within a flow by several thresholds. 4. Calculate
RLIk(f ) and send all RLIk(f ) and RDI (f ) to VG for k ! q QoS mapping.
Feedforward QoS mapping control in VG session/packet based 1. Each user (with one or several sessions or flows) get assigned TB with specified parameters. /* TB enforces each user to keep rate contract between user and VG */ 2. Down-grade to best-effort class marking in DS level for nonconformed packets. 3. Do effective per-flow QoS mapping with the guidance in section III-B. /* mapping of k q to meet Eq. (2) with constraints */
!
class (DS level) based 1. Class-based TB (i.e., inter-connected trTCM in Section IV-A.2) enforces all incoming traffics according to traffic conditioning agreement of SLA. /* Re-mark incoming packets to prevent service inversion among DS levels due to class-load unbalance */ Fig. 4. Dynamic feedforward QoS mapping control algorithm.
The overall feedforward QoS control algorithm at VG is summarized in Fig. 4. A video packet is categorized into k category by RLI/RDI at the video sender, without knowing about other competing flows. An assigned RDI may limit the
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mapping range of q as discussed earlier. Then, how to perform practical and effective QoS mapping based on Eq. (2) is the key issue. First, from Fig. 3, we can obtain a practical mapping guidance for k ! q . Also within the customer network, the traffic conditioning per end-user is handled by VG through assigning TB with different parameters (e.g. the token rate, the bucket size, etc.) as a restriction in order to prevent a user from abusing the customer network resource. Note that the proposed dynamic QoS mapping scheme helps end-users to minimize quality distortion under the total price paid. A.2 Traffic Conditioning and remarking The dynamic QoS mapping for class-based granularity is performed by re-marking through this traffic conditioning function. By degrading the k ! q mapping level when a flow or a class traffic volume exceeds the allowed bandwidth, we can regulate the traffic while trying to meet per-flow efficiency as detailed below. The access network operator can contract with the Internet service provider (ISP) for a traffic conditioning agreement in SLA. Since an end-user is blind to aggregate flows (including flows from other end users) entering the DS domain, we impose the VG to perform traffic conditioning for DS levels on the aggregate flows. The remarking procedure on the basis of the traffic conditioning should be designed on the basis of the services provided by the DS domain and the service level agreement. In this paper, we target our design and simulation for the AF Services. For SLA, we take the example of parameters like the committed burst size (CBS), the committed information rate (CIR), the peak burst size (PBS), the peak information rate (PIR), etc. We assume that the traffic conditioning agreement between access network and the DS domain is the conditioning described by the Two Rate Three Color Marker (trTCM) [8]. We assume that a separate trTCM is operating for each of three AF classes. Each trTCM is composed of two token buckets (C and P for CBS and PBS, respectively) to meter incoming packets, and mark them by one of the three colors (i.e. green, yellow and red). For this paper we consider an example case in which the access network subscribes to three each assured forwarding (AF) classes and each AF class further differentiates the packets into three kinds of drop precedence. The access network can also used the best-effort service. We denote the resulting DS levels (behavior aggregates) by BE and AF xy . BE denotes the best-effort level. AF xy denotes the behavior aggregate of AF class x 2 f1; 2; 3g and drop precedence y 2 f1; 2; 3g. We assume that our QoS parameter q is associated with DS levels; BE; AF33 ; AF32 ; AF31 ; AF23 ; AF22 ; AF21 ; AF13 , AF12 ; AF11 are equivalent to numbers of q = 0 9 in this work. (We are assuming that the lowest drop precedence of AF class 1 provides better packet drop probability than the highest Drop precedence of AF class 2, etc.) For adopting trTCM, we use the following color code: green - corresponding to DS level AFx1 ; yellow - corresponding to DS level AF x2 ; red - corresponding to DS level AF x3 . We use the color-aware mode of trTCM for the remarking. As k ! q mapping is done prior to the aggregate Traffic conditioning (Fig. 5), and as the value of q specifies the color and the AF class, the traffic input to the trTCM can be considered already color-coded. In the color-aware mode, packets arrive with some pre-assigned color. The initial assignment is respected, and the drop precedence can only be increased. Token buckets P and C are initialized to PBS and CBS in the beginning. Token bytes B C (for the C token bucket) and B P (for the P token bucket) are incremented by CIR and PIR up to their upper bounds, CBS and PBS, respectively. If a packet of size B has been colored as red, it will remain red. A yellow packet is re-marked as red if there is no token available in both C and P buckets. Otherwise, it is allowed to remain yellow and B p becomes BP B if there is a token in the P bucket. Finally, a green packet becomes yellow with BP BP B if a token is available only in the P bucket. Or it becomes red if there is no token available in either buckets. Otherwise, a green packet remains green with B C BC B and BP BP B . In order to improve the end-to-end quality of video delivery beyond just satisfying the traffic conditioning agreement, we modify the individual trTCMs into ”inter-connected trTCM”. To coordinate the inter-connected function between trTCMs, the aggregated ingress rate into each class is measured. If the aggregated rate reaches the maximum assigned rate Cj for each class queue with a typical WFQ scheduler as shown in Fig. 5, we hand over the incoming packet randomly. The aggregated ingress rate is calculated by using the exponentially weighted moving averaging to smooth the estimation of fluctuation noise. To avoid possible sensitivity due to the packet length distribution, a dynamic weighting as a function of inter-packet arrival time is utilized. Thus, the average rate is measured via
avg rate = (1
einter pkt time=T ) current rate + einter pkt time=T avg rate;
where T is a constant. We refer to [2] for more details regarding the choice of T . In the inter-connected trTCM, if the aggregated rate for the queue of class j is greater than C j , the incoming packets into this queue is re-directed by trTCM to a lower class queue with the probability of (avg rate C j )=avg rate and so
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Aggregate rate measurer Per-flow TB Packets with categorized RPI in each flow
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Fig. 5. Inter-connected trTCM with aggregated rate measurements.
on. When a packet is re-directed to a lower AF class, the color is set to green in that lower class. With the above schemes in place, we believe that the VG can reduce the probability of end-to-end service inversion (a lower class flow happens to receive better service than a higher class one), which will be suggested in the simulation result to be presented in section V. In fact, we plan to investigate the merits of including the inter-connected trTCM as a part of traffic conditioning agreement. If all access points to the DS domain exercises trTCM, the probability of service inversion within the DS domain may be greatly reduced. B. Dynamic Feedback QoS Control Feedback control is performed by the VG with the support of end-systems to adjust the QoS mapping level of RLI to category k and RDI level for delay quality upon the feedback of video receiver. Receiver sends a report of delay/packet loss to the sender whenever necessary and can ask the mapping adjustment when the received quality is not satisfactory. This feedback control enables the fine-tuning of rather coarse feedforward QoS mapping. Video receiver may ask even the decrease of QoS mapping level when it feels the current received quality is over-provisioned and wants to reduce the charging bill. This feedback mechanism enables the whole QoS control to be adjusted better and to remain stable by sustaining the end-to-end quality within an acceptable range. The algorithm for the feedback QoS control is again summarized in Fig. 6. Receiving end-system 1. Receiver requests service with a tolerable range /* e.g., 400msec average delay 500msec variation, and/or 3% average loss rate 5% */ 2. Send increase(+),decrease(-), or no-change indication in QoS mapping as a periodic feedback to VG through corresponding end-system /* When average delay/loss rate deviates from desired ranges, feedback reports the necessary QoS mapping change request. */
Reaction of VG for feedback QoS mapping control 1. Corresponding end-system forwards a or no-change indication to VG 2. VG changes one step (+), (-) or no-change in QoS mapping of k q with the indication.
!
Fig. 6. Dynamic feedback QoS mapping control algorithm.
V. S IMULATION R ESULTS The proposed QoS mapping framework is evaluated by simulations in the several steps. For the video streaming, error resilient version of ITU-T H.263+ stream is utilized and decoded by error robust video decoder. All other components are then simulated by ns (network simulator) [9]. A. Feedforward QoS Mapping Control at VG with Inter-connected trTCM We investigate the VG functionality for dynamic feedforward QoS control with aggregated video flows. Network topology shown in Fig. 7(a) is used to generate network dynamics. The network topology is quite simplified to obtain
bp s,
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Fig. 7. (a)Network topology model for the simulation, (b)Comparison between RPI-blind and RPI-aware mapping over different imposed loads (endto-end video performance: PSNR). The vertical bars show the standard deviation magnitude of PSNR.
initial intuition out of the experiments. All the sources travel through the same path, and there is no other traffic than the traffic from the sources. Each source transmits a video stream from the video trace. TABLE I N ETWORK LOAD CONDITION FOR THE PERFORMANCE EVALUATION OF INTER - CONNECTED TRTCM. class queue
AF1 AF2 AF3 BE
subscription level(%) 136 123 104 N/A
assigned link BW by WFQ(kbps) 1200 900 600 300
imposed load(kbps) 1632 1104 624 240
TABLE II P ERFORMANCE OF INTER - CONNECTED TRTCM FOR TOTAL AGGREGATED FLOWS IN EACH CLASS QUEUE . N OTE : *) FROM PER - USER TB ENFORCEMENT, **) U SUALLY FROM TRAFFIC CONDITIONING OF INTER - CONNECTED TRTCM. without inter-connected trTCM flows in class queue
AF1 AF2 AF3 BE
achieved throughput (kbps) 1223.6 918.7 612.0 240
packet loss rate(%) 28.43 20.59 2.70 0.41
delay/jitter (msec) 142.0/8.19 92.3/7.24 127.3/19.14 138.0/18.00
with inter-connected trTCM re-marking* rate (%) 2.20 5.44 3.11 0
achieved throughput (kbps) 1577.6 960.0 331.5 125.1
packet loss rate(%) 1.48 8.79 46.38 49.78
delay/jitter (msec) 67.1/8.33 70.3/15.70 117.6/16.06 138.0/17.99
re-marking** rate (%) 44.35 40.91 36.75 0
The main objective of experiments in this section is to see the effectiveness of the inter-connected trTCM, so we do not use the static QoS mapping described in section IV. Instead, a fixed DS-level is assigned to an entire individual video flow. Several video flows with different DS-level are fed into the inter-connected trTCM. The test is designed to see the effectiveness of the inter-connected trTCM in preventing service inversion among multiple classes of queues. Without the inter-connected trTCM, the packets in the highest class (AF 1 ) may experience more delay than those in the lower-class queue (e.g., AF 2 ) if the high-class queue is overloaded. The reason is that the isolated trTCM only changes the drop precedence ’within’ the class without downgrading the packet’s class. To show the performance of inter-connected trTCM, the experiments are designed to give unbalanced traffic load into the class queues, as specified in Table I. The ”imposed load” is designed so that the higher class has more loads. Three ‘Hall’ (average rate per flow : 288kbps) and two ‘Foreman’ (384kbps) video streams are assigned to AF 1 class queue. One ‘Foreman’ and three ‘Akiyo’ (240kbps) are assigned to AF 2 , one ‘Foreman’ and one ‘Akiyo’ are to AF 3 , and one ‘Akiyo’ to BE class queue, respectively. For all the flows assigned with the AF class, the color is set to green. Note again that higher class queues are loaded deliberately heavily (compared to assigned bandwidth portion by scheduler) to cause service inversion. This is to demonstrate the promising regulation effect of the inter-connected trTCM for the dynamic network load conditions. To give complete explanation of Table I, ”subscription level” is defined to be the 100*(imposed
9
load/assigned link BW). Also, for this experiment, we set the trTCM’s PIR to be equal to the bandwidth assigned by the weighted fair queueing discipline, which is exercised at the border link. The results in Table II show that the inter-connected trTCM of VG is useful for preventing service inversion. For example, for the case of ”without inter-connected trTCM” a higher class has a higher ”packet loss rate”; thus, demonstrating the service inversion. For the case of ”with inter-connected trTCM”, a higher class has a better ”packet loss rate”. The same effect is shown for the ”dealy/jitter” measurements. Table II also shows the rate of re-marking out of each class. The statistics in Table II does not count the change of q value (re-marking) within the same AF class. As expected, with inter-connected trTCM, much re-marking takes place when the high-class queue is overloaded. The re-marking shown for the case of ”without interconnected trTCM” is not due to remarking at its trTCM. Its tTCM does not re-mark across AF classes. The re-marking statistics for the case of ”without interconnected trTCM” is purely due to the per-flow traffic violation at the TB (downgrade to the best-effort class), which is prior to the trTCM. Next, with the inter-connected trTCM in place, we compare RPI-aware with RPI-blind mapping. In the color-blind mapping every packet prior to the inter-connected trTCM, except for the one that violates the TB, is assigned with q = 4 (AF23 ). The RPI-aware mapping follows the guidelines in section III-B. In this experiment, we only use ‘Foreman’ video streams. In every trial of this experiment, we only one stream uses RPI-blind mapping, which is used as a reference-tobe-compared stream. (We denote this stream by F r .) Different numbers of identical flows (all ’Foreman’) with RPI-aware QoS mapping are used for different simulation run; thus, resulting in different total traffic loads, as used for the horizontal axis of Fig. 7(b). We then select one sample flow (which we denote by F s ) among the RPI-aware flows to compare its PSNR with that of RPI-blind F r . (Due to symmetry, the PSNR of all the streams with RPI-aware mapping should be more or less the same.) ‘Foreman’ video stream with the mapping guidance in Section III-B is used matching the same total cost constraint of RPI-blind flow for comparison. As shown in Fig. 7(b), the network load increases in accordance with the increasing number of RPI-aware flows for different simulations runs, and the overall PSNR for selected RPI-aware flow f s is degraded gracefully compared to that of RPI-blind flow f r . Also, the RPI-bind flow experiences irregular end-to-end visual quality according to load levels reflected in the average/standard deviation of average PSNR, where the vertical bar stands for the standard deviation. This implies the higher k category packets of RPI-blind flow can not be protected properly, sometimes lost more than lower k packets, and the loss effect appears severely, while RPI-aware mapping case protects the packets according to the importance order k . Moreover, let us observe the loss rate of f r and fs at 115.2% load level from Fig. 7(b). The total loss rate of f s is 13.71% while that of f r is 8.57%. The objective PSNR quality measure of f s is better than that of f r over all time even though fs experiences a worse total packet loss rate as shown in Fig. 8(a). This is due to the fact that f s tends to lose relatively unimportant packets due to RPI-aware mapping. 40 35 PSNR(dB)
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Fig. 8. (a) RPI-blind and RPI-aware mapping comparison for ‘Foreman’ sequence by PSNR. A sample 52th frame of visual quality comparison at 115.2% load, (b) RPI-blind (c)RPI-aware mapping.
In Fig. 8(a), we show the loss rates of f s for different source categories. Recall that all k source categories of f r is mapped into q = AF 23 . With this cost constraint, fs follows QoS mapping guidance. The objective PSNR quality measure of fs is however better than that of f r over all time even though f s experiences a worst total packet loss rate as shown in Fig. 8(a). It is again verified by the presented snap shots of a decoded video frame as shown in Fig. 8(b) and 8(c). RPI-aware mapping is a clear winner in the average or instance sense over time-varying network load conditions.
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Fig. 9. Feedback control effect by average loss-rate comparison with updated interval of 0.5sec. (a) Reference case with a video flow staying to a fixed DS level (q = 2) and (b) Feedback case with a video flow adjusting DS level.
Finally, we verify the effectiveness of the proposed feedback QoS mapping. With other eight flows of ’Foreman’ sequence using RPI-aware mapping (i.e., total cost is equal to the mapping of all k to q = 4), one test ’Foreman’ video flow is compared in both cases: one is static DS mapping (i.e., all packets are mapped to q = 2, the other case is dynamic QoS mapping using proposed feedback QoS mapping control. The k ! q QoS per-flow mapping at VG is adjusted based on periodic feedback from the receiver with a desired loss-rate range. In simulation, the range is set to [5, 15%] and we expect to get the average loss-rate as the middle value (i.e. 10%). Obtained results illustrate how feedback QoS control affects the average loss-rate dynamically. As shown in Fig. 9, VG can re-mark the DS level dynamically upon receiver’s feedback. The average total loss-rate of a reference case is 13.4% and the loss-rate of the feedback case is 9.5%, respectively. The time interval of QoS feedback and re-marking is 0.5 sec for both cases, and the time interval for the average loss-rate calculation in Fig. 9 is the same. VI. C ONCLUSION This paper discusses the QoS mapping between categorized-packet video and proportional DiffServ network within relative service differentiation framework. In order to show the performance of VG, we set encoded video stream as an example of streaming media in simulated DiffServ network. Our previous works [7, 10] showed that the QoS mapping between categorized-packet video and DS level can give the gain of end-to-end video quality in terms of delay and loss under total cost constraint. However, those works are limited to per-flow based feedforward QoS mapping. This present paper extends them to full-scope, having three granularities of session/packet/class and feedforward/feedback QoS control. R EFERENCES [1] [2]
S. Blake, D. Black, M. Carlson, E. Davies, Z. Wang, and W.Weiss, “An architecture for differentiated services,” RFC 2475, IETF, Dec. 1998. I. Stoica, S. Shenker, and H. Zhang, “Core-stateless fair queueing: Achieving approximately fair bandwidth allocations in high speed networks,” in Proc. ACM SIGCOMM, (Vancouver, Canada), Sept. 1998. [3] I. Stoica and H. Zhang, “Providing guaranteed services without per flow management,” in Proc. ACM SIGCOMM, (Boston, MA), pp. 81–94, Sept. 1999. [4] C. Dovrolis, D. Stiliadis, and P. Ramanathan, “Proportional differentiated services: Delay differentiation and packet scheduling,” in Proc. ACM SIGCOMM, (Boston, MA), Sept. 1999. [5] C. Dovrolis and P. Ramanathan, “Proportional differentiated services, part II: Loss rate differentiation and packet dropping,” in Proc. International Workshop on Quality of Service, (Pittsburgh, PA), June 2000. [6] J.-G. Kim, J. Kim, J. Shin, and C.-C. J. Kuo, “Coordinated packet level protection employing corruption model for robust video transmission,” in Proc. SPIE Visual Communications and Image Processing, (San Jose,CA), Jan. 2001. [7] J. Shin, J. Kim, and C.-C. J. Kuo, “Relative priority based qos interaction between video applications and differentiated service networks,” in Proc. IEEE International Conference on Image Processing, (Vancover, Canada), September 2000. [8] J. Heinanen and R. Guerin, “A two rate three color marker,” RFC 2698, IETF, Sept. 1999. [9] UCB/LBNL/VINT, Network Simulator - ns (version 2). 1998. [10] J. Shin, J. Kim, and C.-C. J. Kuo, “Content-based packet video forwarding mechanism in differentiated service networks,” in Packet Video Workshop’2000, (Sardinia, Italy), May 2000.
