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RELATIVE PRIORITY BASED QOS INTERACTION BETWEEN VIDEO APPLICATIONS AND DIFFERENTIATED SERVICE NETWORKS Jitae Shin, JongWon Kim and C.-C. Jay Kuo

Integrated Media Systems Center and Department of Electrical Engineering-Systems University of Southern California, Los Angeles, California 90089-2564

ABSTRACT

In this research, we investigate Quality of Service (QoS) interaction provisioning between video applications and components of the DiServ network. QoS interaction is performed through the mapping of video packets based on the relative quality index (RQI), which represents the relative preference in terms of loss and delay, onto the adaptive packet forwarding mechanism in a DiServ network. To verify the eÆciency of the proposed strategy, the end-to-end performance is evaluated through error resilient packet video transmission over a simulated DiServ network. Results show that the proposed QoS interaction framework result in an enhanced end-to-end video quality at the same cost constraint. 1. INTRODUCTION

To overcome the inherent limitation of the best-eort Internet, dierentiated services (DiServ) for dierent applications are being considered by the Internet Engineering Task Force (IETF). In the DiServ model, resources are allocated dierently for various aggregate traÆc ows. Consequently, the DiServ approach allows dierent QoS levels to dierent classes of aggregated traÆc ows based on the the service pro le. With the DiServ-enabled network, the integrated approach by using the DiServ-aware applications increase enduser's satisfaction without adding network capacity. The overall diagram of the proposed interaction mechanism in a DiServ network is shown in Fig. 1, where service dierentiation is mainly provided in terms of the loss probability and delay associated with the forwarding queues. The basic QoS 2-tuple fdelay, lossg can be requested dierently per each source packet or per user application level. For coordinated service dierentiation, video streams should rst be classi ed in the loss and/or the delay preference. Then, the DiServenabled network provides relatively dierentiated QoS levels according to the loss/delay preference. In this paper, the relative priority based QoS inter-

R Q I(Relative Q uality Index) in term s of Lossor D elay Preferences am ong users orw ithin flow

U ser N etw ork

D iffServ D om ain

SLA

N etw ork Q oS Levels

Classifier

Traffic Conditioner

W FQ as

Scheduler Q ueue m anagem ent w ith m ultiple RED (orRIO )

Figure 1: The framework of the proposed interaction between relative quality indices and network DiServ levels. action will play a key role in maximizing the end-toend video quality under a given cost constraint. To be more speci c, each video ow of a user application will demand its loss/delay preference by marking its DiServ byte with the Relative Quality Index (RQI). This index is further divided into the relative loss quality index (RLI) and the relative delay quality index (RDI) for loss rate and delay, respectively. These categorized packets in video streams are forwarded toward the destination through a packet forwarding mechanism that is composed of a classi er,queue management scheme and scheduling. 2. VIDEO STREAM CLASSIFICATION

For packet video applications, the relative priority assignment for each packet should re ect the in uence of each packet to the receiving end-to-end video quality. First, the classi cation of video streams in terms of delay preference (i.e., RDI) depends more on the application context (e.g., video-conferencing or video on demend (VOD)) rather than the video contents within a stream. The loss and delay attributes are however not

orthogonal, rendering the classi cation problem more complex. By considering the weighted subjective measure for the delay attribute, multiple delay layers within a stream are plausible [1], where varying delay (i.e., end-to-end latency) requirements are quanti ed by perceiving a motion event such as nodding or gestures, or maintaining lip sync. Furthermore, we can classify packets of I-,P-, or B- compressed frames of ISO MPEG from the inter-frame aspect. This is closely related with loss preference, too. Since the RDI is most likely related to the application context, we will focus on the application level when referring RDI in this research. For RLI, recent research on the corruption model [2] attempts to model the loss eect of video streams and provides a viable solution to relative prioritization. However, most modeling eort up to now has focused on the statistical side of the loss eect while a dynamic solution is considered in our approach. Several video factors have been taken into consideration in the design of the RLI value to re ect the error propagation eect. The magnitude and the direction of the motion vector of each maroblock (MB) can be used to indicate the loss propagation eect due to motion. The refresh eect of the I-MB can also be included for the same purpose. One can also measure another more direct video factor called the dependence count, which counts how many times a MB is referenced. The pattern of the dependence count is closely related to RLI pattern in our experiment. The RLI assignment for a packet R LI 10.00 9.00 8.00 7.00 6.00 5.00 4.00

Thresholds

3.00 2.00

intra-refreshed, inter, etc), the associated motion vectors (MVs), the total size in bytes, and the existence of any picture-level header. That is, the RLI represents the summarized level of its component priority levels weighted by their relative importance as RLI

=

X

NNV F i=1

Wi

where N V F stands for the normalized video factors and Wi is the corresponding weight factor. The compressed H.263+ video stream is generated at a target rate of 384kbps for a CIF test sequence with 10 fps. Several error resiliency and compression eÆciency options ('Annexes D, F, I, J, and T' with random Irefresh) are used in the so-called 'Anchor' (i.e., GOB) mode for video generation. The random I-refresh rate is set to 5% to cover the network packet loss. It is then packetized by one packet per GOB while RLI is assigned according to Eq. (1). Fig. 2 shows the RLI assignment example for the 'Foreman' sequence, which results in a distribution similar to that obtained by using the time-consuming and non-causal dependency counts in the experiment. 3. QOS-BASED DIFFSERV FORWARDING

The multiple random early detection (RED), RED with in/out bit (RIO) queue management and weighted fair queueing (WFQ) scheduling provide the dierentiated forwarding in our queuing system. WFQ provides a weighted portion of the shared bandwidth to each class queue. Within a class queue, RIO provides a dierent drop preference by using a dierent RIO (or multiple RED) parameter [3]. The parameter set includes the minimum threshold, the maximum threshold, and the maximum drop probability (minth; maxth; maxp ) for each IN or OUT control curve, respectively.

1.00

A daptive Scheduler

A ctive D ropper

0.00 1

1001

2001

3001

4001

(1)

 N V Fi ;

5001

lp(t)

GOB PacketNum ber

Figure 2: RLI for the 'Foreman' CIF sequence with the proposed video factors for each GOB packet. would be best if it can precisely represent its error propagation eect to the receiving video quality. Here, we propose a simple yet eective RLI scheme for the error resilient H.263+ video stream. Basically, RLI is calculated for each packet corresponding to its GOB. It takes into account video factors of its component MBs. These factors consist of the MB encoding type (intra,

D rop C ontrol

w i(t)

LossProbability ControlA lgorithm

Average queue length

RIO Q ueue M anagem ent

Service Rate ControlA lgorithm

Filtered Q ueue Length

D elay C ontrol

q A daptiveW eighting factorofW FQ Scheduling

Figure 3: The control algorithm loop for loss and delay service dierentiation. However, due to the static nature of RIO and WFQ weighting factors, a straightforward integration of RIO

and WFQ is not suÆcient to provide stable and persistent service dierentiation in short-term time resolution [4]. Thus, we develop an adaptive packet forwarding mechanism, focusing on the relative packet loss/delay service dierentiation based on multiple RED (or RIO) and WFQ. The control loop algorithm for the proposed loss and delay service dierentiation is shown in Fig. 3. The packet loss starts to occur at a network node when Pincoming packet rates exceed the outgoing link rate, i i (t)  C . To maintain the relative loss/delay dierentiation promised in the network service level agreement (tied with RQI), weighting factors of WFQ with RIO are controlled dynamically to provide stable and persistent network QoS forwarding. Delay service dierentiation is mainly done via the adjustment of the scheduling component. The desired relative delay ratio is assumed to be d1 : d2 : d3 :


 Æ1 : Æ2 : Æ3 :


; where di is the relative average target delay in class i. Then, the controller input wi can be calculated as q1 (t) q2 (t) d1 (t) : d2 (t) = : = Æ1 : Æ2; w1 (t) C w2 (t) C Æ1 q2 (t) w2 (t) = w1 (t) ;


; Æ2 q1 (t) where di (t) is the local range version of di at measured time t on updated time interval [t
; t]. Under the assumption P wi (t) = 1, we have 

i

  ( ) Æ1 qn (t) 1 + Æ2q1(t) +


+ Ænq1(t)  w1 (t) = 1: Then, we can obtain the adaptive weighting factor via qi (t) : wi (t) = n P Æi q  ( t ) k Æk Æ1 q2 t

k=1

The proposed scheme is compatible with any kind of scheduling schemes, and this weighting factor can adjust the relative delay/loss quality among dierent class queues adaptively based on the network conditions. 3.1. QoS Mapping

The quality request of video packets has two dimensions as shown in Fig. 4. They can be combined with dierent weights according to applications. The network DS level provides dierent relative delay and packet loss rates on each class queue with three dierent drop preferences by using multiple RED. For example, one premium service (PS), four assured service (AS) with

three drop preferences, and one best eort (BE) class queues are de ned in IETF RFC 2597 and 2598. Under these environments, the interaction starts in the form of the marking of two quality indices of each packet on to the discrete category of the DiServ (DS) byte indexed via k. These categorized relative indices are then classi ed, conditioned, and assigned (i.e., re-marked) to a certain network DS level q based on the traÆc pro le from service level agreement (SLA) at the boundary region (i.e. at the edge router). Each packet assigned to a certain q level will get a speci c reliability (e.g. the packet loss rate Lq and delay quality level Dq ) by paying the unit cost pq . This QoS mapping can be formulated as an optimization problem as detailed in [5]. Relative Delay RLI(Loss Sensitivity)

(A)

(B)

Vide

o Pac

0 X

RDI(Delay Sensitivity)

ket N

umbe

rs

Video Source Categories: k

(Re)Marking QoS Mapping

Service Level Agreement (SLA)

4

X

2

1

X

X

3

X

AS1

BE

(B)

AS2

(A) Relative Loss Rate

Network QoS Level : q

Figure 4: Forwarding interaction between relative quality indices and network DS levels. In our experimental set consisting of 5 categorized video sources, we have two AS queues with RIO and one BE class queue, which provides 5 X-points, k = 5 and q = 5. Video source categories can choose dierent delay or loss preference following the same cost line. For example, the delay preferred source category can choose the worst drop DS level of the upper class queue at the expense of a higher packet drop rate. Under the same cost constraint, case (A) video application like video-conferencing needs to be assigned better class queues like AS1 while delay-tolerant video application in case (B) like VOD can be assigned to an inexpensive class queue like AS2 or BE . Let us compare two kinds of mapping (with multiple q or a single q) in each case. Case (A) shows the comparison with a single q (2,2,2,2,2)1 vs. multi-queue (1,1,2,4,4) and case (B) with (1,1,1,1,1) vs. (0,1,1,2,2). We see a clear advantage of QoS mapping into multiple network q level under a similar total cost represented by total average delay and packet loss rates as illustrated in the next section. Finally, when one may 1 Each source k-categories (k0,k1,k2,k3,k4) classi ed by RLI and thresholds are assigned into network QoS level q number as shown in each component. The priority is given in an increasing order. For example, the k2 source category is mapping to q=2 in (1,1,2,4,4).

Performance Parameters Avg./Var. PSNR (dB) Average Loss Rate(%) Avg./Var. Delay (ms)

case (A) (2,2,2,2,2) (1,1,2,4,4) 26.80/2.880 28.77/2.743

case (B) (1,1,1,1,1) (0,1,1,2,2) 26.03/3.270 26.44/2.940

2.46 81.47/19.266

5.31 94.04/25.23

3.37 80.88/17.945

5.60 112.50/27.70

scatter packets of a single stream over multiple queues, the cost should incorporate the added complexity cost and out-of-order arrival handling cost at the other end, which is assumed negligible at the current stage. 4. EXPERIMENTAL RESULTS

The overall description for interaction of video and network is shown Fig. 5,6. Two 'Foreman' video traces are used and one is mapped into a single q, the other is into multiple network q levels. Experimental data are given in Table 1. Video Packetization

of encoded video stream

Standard H.263+ encoder

Classifier with RQI

Simulated Network model

rates are closely related with the assigned class queue. The total cost is assumed to be proportional to the total loss rate. The multiple q case shows better visual quality as well as PSNR in case of not exceeding the total loss rate of the corresponding single q case. In delay-sensitive case (A), the multiple q set gets better PSNR and similar delay/jitter by assigning higher k source categories into higher network q even though it experiences a higher packet loss rate (i.e. a lower total cost) than the single q set. Also, the multiple q in case (A) gets better average delay and delay jitter than the single q. The comparison of perceptual video quality between multiple queue (MQ) and single queue (SQ) mapping is shown in Fig. 7. In delay-tolerant case (B), MQ has the same bene t in average PSNR and get larger delay/jitter than SQ. However, it is tolerable to delay-insensitive video application like VOD. The visual quality in case (B) is similar to that in case (A). The each packet loss rate of k categories in cases shows the proposed adaptive packet forwarding mechanism provides more stable proportional packet loss rate according each q level than static WFQ. 40 35

PSN R _Y (dB )

Table 1: Performance Comparison in case (A): relative delay-sensitive and case (B): relative delay-tolerant video applications

30 25 20

Evaluate end-to-end video quality by PSNR

Service Level Agreement

N o Loss Experience Network delay and packet loss

Reconstructed video

Figure 5: The overall simulation diagram for video applications in a DiServ network. Video Trace

10Mbps, 0.1msec

R0

10 Mbps 3msec

R2 Other TCP traffics

R1

Traffic 3 Mbps 20msec

R3

Single q:2

1

51

101 151 201 G O B fram e num ber

251

Figure 7: Visual quality comparison between multiple queues and the single queue mapping in case (A) with the 'Foreman' CIF sequence.

Corrupted video packets

Standard H.263+ decoder

M Q of(A)

15

Sinks

10 Mbps 3msec

Video Trace

Figure 6: The DiServ network model used in simulation. The unit cost for each network QoS level q depends on the service provider. Both delay and packet loss

5.

REFERENCES

[1] Y. Chang, D. Messerschmitt, and T. Carney, \Expanding network video capacity with delay cognizant video coding," in Proc. Visual Comunications and Image Processing, (San Jose, CA), Jan. 1999. [2] G. Reyes, A. Reibman, and S. Chang, \A corruption model for motion compensated video subject to bit errors," in Proc. of Packet video Workshop, May 1999. [3] D.Clark and W.Fang, \Explicit allocation of best eort packet delivery service," IEEE/ACM Trans. on Networking, vol. 6, pp. 362{373, August 1998. [4] C. Dovrolis and P. Ramanathan, \A case for relative differentiated services and the proportional dierentiation model," IEEE Network, September 1999. [5] J. Shin, J. Kim, and C.-C. Kuo, \Content-based packet video forwarding mechanism in dierentiated service networks," in Packet Video Workshop'2000, (Italy), May 2000.