Streaming MPEG-4 Video over Differentiated Services Networks

Transcription

1 Streaming MPEG-4 Video over ifferentiated Services Networks Fei Zhang, Mark R. Pickering, Michael R. Frater, and John F. Arnold. School of Electrical Engineering, University College AFA (UNSW), Canberra ACT Abstract We investigate streaming MPEG-4 video over relative ifferentiated Services (S) networks that can provide Q aggregated classes ordered in a way such that class is better or at least no worse than class (-) for < Q, in terms of packet loss. We propose an algorithm to measure the loss impact of a video packet on the uality of the decoded video images. We show how the optimal Quality of Service (QoS) mapping from the video packets onto a set of available S classes depends on the loss rates of the S classes for a given pricing model. The performance of our algorithms is evaluated through experimental tests and compares favourably to previous work.. ntroduction n recent years, there has been a growing demand for streaming multimedia applications over the nternet. n particular, video communication over the nternet has attracted much research interest. n order to achieve improved uality in real-time or near realtime video communications such as videoconferencing, video-telephony, and video-ondemand, the users must be provided with higher levels of Quality of Service (QoS) guarantees (e.g., low-loss and low-delay) than the current P nternet offers. The current nternet is of best-effort type, forwarding data packets at the network layer with no guarantee or preference for reliability or timeliness of delivery. This same-service-to-all paradigm has become increasingly inadeuate for nternet applications (including streaming video) that have diverse QoS reuirements. Nowadays there is a widespread consensus that the nternet architecture has to be extended with service differentiation mechanisms so that users/applications may have a range of QoS choices for packet delivery. n this effort, the nternet Engineering Task Force (ETF) has proposed two distinct approaches for service differentiation: the ntegrated Services (ntserv)[] and ifferentiated Services (iffserv)[2] architectures. However the ntserv architecture has received very limited acceptance among the network community due to its problem of non-scalability and non-manageability (see [3] for details). On the other hand, the iffserv approach is more recent. The main goal of this architecture is to provide a scalable and manageable network with service differentiation capability. n contrast to the per-flow-based QoS guarantee of ntserv, iffserv networks provide QoS assurance on a per-aggregate basis, whereby data packets are classified into one of a small number of aggregated classes, each of which can be identified with a six-bit iffserv CodePoint (SCP) located in the P packet header [2]. Routers at the edge of the iffserv domain have a traffic conditioner [4,5,6] that re-marks the packets (changes the SCP) according to certain traffic management policies. The configuration of the core routers of a iffserv domain is less complicated than that of the edge routers. The core routers do not have traffic conditioners, and they simply forward packets using a Per Hop Behaviour (PHB) that corresponds to the SCP of the packets. The ETF has proposed two PHB schemes to provide differential treatment to P packets. One scheme is called Expedited Forwarding (EF) [7] which is designed to build a low loss, low latency, low jitter, and assured bandwidth service. The other is called Assured Forwarding (AF) [8] which intends to provide different levels of forwarding assurance for P packets. However, the iffserv paradigm is still actively evolving, and some major issues remain the subject of much debate (See,e.g., [9-6]). The nternet research community has been proposing and investigating different approaches to achieve differentiated services. n particular, a lot of effort has been devoted to the problem of how to achieve service differentiation in terms of ueuing delay and packet loss [9-6], both of which are of primary concern for multimedia applications over the next generation iffserv-capable nternet [7-23]. n this paper, we study streaming MPEG-4 video over iffserv networks that can provide relative differentiated services [3,3,4]. Such a network offers Q aggregated classes that are ordered such that class

2 is better or at least no worse than class (-) for < Q, in terms of local (per-hop) metrics for the ueuing delay and/or packet dropping. We first examine the impact of a lost video packet on the uality of the decoded video images. Then we investigate the optimal mapping from the data packets onto the iffserv classes to achieve maximal video uality subject to a given price constraint. We show how the optimal mapping depends on the loss rates and unit prices of the S classes. We demonstrate that the new algorithms perform better than those proposed in Refs [7,8,9]. 2. Packet Loss mpact on Video Quality The MPEG-4 video coding standard specifies that the encoder have an error-resilient mode that enables it to compress a video seuence into a stream of video packets with approximately the same pre-set size. These video packets are coded independently and separated by a byte-aligned synchronisation marker, so that the decoder can correctly decode received packets even if some previous packets are lost or corrupted. We observe that the loss of some packets would result in greater uality degradation than the loss of some others because the uality will depend on how well the error can be concealed in the decoder. For example, if a video packet contains many scene change-induced ntra macroblocks, it would be difficult to conceal the loss of such a packet with either temporal or spatial concealment method. Another very important type of packet includes those with a Video Object Plane (VOP) header, which contains information such as coding type (, P, or B-frame) and time stamp. The loss of a VOP header would result in an entire VOP being undecodable. n order to use a iffserv network effectively, it is important to know the loss impact of every packet, so that those packets with higher loss impact may be sent via an appropriate higher priority traffic class that suffers lower loss probability. (Here we only consider the loss effect. We assume delay and jitter effects can be absorbed by using a large play buffer in the decoder). To measure the loss impact of a video packet uantitatively, we assume that the decoder is using a normative error concealment algorithm: For the first -frame we use the usual interpolation method to perform error concealment. For all subseuent frames we employ a temporal concealment scheme by using a neighbouring MB s motion vectors, if available, or zero motion vector if no neighbour macroblock (MB) is available. Under such an error concealment scheme, if a packet is lost in the k-th frame, then the initial error will be k = f i i 2 δ, (2.) where the sum is over all the errored pixels, and δ fi is the difference between the decoded signals with and without the error due to the packet loss. Such an initial error will propagate into the next frame (k+) when the errored pixels are referenced 2 = n( i) δ (2.2) k+ f i i where n(i) is the number of times the pixel i is referenced by the (k+)-th frame. This temporal error propagation may only be stopped by ntra-refresh or - frames. We consider an MPEG-4 compressed video seuence with periodic -frames (-frames can provide errorrefresh, and they can be used to create trick modes such as fast-forward and fast rewind in applications like video-on-demand). f we assume that k+ k, then the total suared error distortion due to the packet loss in the frame, which is k frames away from the immediately previous -frame, would be ( N k) k, where N is the period of the - frame (i.e., an -frame appears regularly once every N frames). Therefore, we define the loss impact of a given packet in the k-th frame as ( N k ) k. This loss impact correctly reflects the fact that a packet loss in an -frame will generally result in a greater impact on the video uality because the errors have a longer effect. The present method is simpler than that proposed by Shin et al [7], where three factors were taken into account in determining the relative priority index of a video packet. The three factors are the initial error, the average motion vector size and the number of ntra macroblocks contained in a packet, with weights of 0.7,0.5, and 0.5 respectively. n section 4 we will compare the performance of these two methods. 3. Optimal QoS Mapping We consider N consecutive video packets, each of which has a loss impact denoted as i. We assume that there is a set of available S classes {, Q }, each of which experiences an average loss rate l and attracts a unit price P. Without loss of generality, we assume that the S classes are ordered such that the loss rate is decreasing and the unit price increasing: l > l2 > > lq and P <P 2 < <PQ.

3 To send a video packet stream over the iffserv network, we must first assign each packet i with an appropriate S class i (). Similar to Ref. [7] we call such a mapping (from the packet to the S classes) QoS mapping. The problem is to find an optimal QoS mapping that satisfies the given price N constraint, () i= P i P, where P is the budget for the N packets, and minimize the loss impact N expressed as (). Such an optimal QoS i= l i i mapping would also maximize the receiving video uality. To solve this optimisation problem, we first sort the N packets into ascending order according to the loss impact. Then it can be shown that the optimal mapping must be an ordered mapping such that the first K packets are mapped to the lowest priority S class, the next K2 K packets to the second lowest level S class, and so on. Therefore, the total loss impact can be expressed as J( K, K,, K ) = 2 Q K K N k k K k KQ l l l k 2 k Q k = = + = + And the price constraint becomes 2 2 Q (3.) π ( K, K2,, KQ ) = K P + ( K K ) P + + ( N K ) P P, Q (3.2) This is an optimisation problem with (Q-) integer decision variables K K2, KQ N. The feasible solution region is defined by the price constraint (3.2) and the bandwidth constraints of each S level based on traffic conditioning agreement. f only two S classes are available, the optimisation problem can be solved analytically. K NP2 P P P 2 (3.3) This euation clearly indicates that when the user is paying the base price P = NP, then K = N, meaning that all packets have to be sent via the low S class (best-effort class). When P is increased the number of low priority packets K will be decreased, and the number of high priority packets will be increased. n the extreme case where the user can pay the top price P = NP2, then K = 0, which means that all the packets could be sent via the high priority class provided there was no bandwidth limit in the high class. When there are more than two S levels to choose from, the optimal solution can be analysed with the method of Lagrange Multipliers. We consider the Lagrangian function, L= J( K, K2,, KQ ) λπ [ ( K, K,, K ) P] 2 Q (3.4) Let the first-order derivatives of this function with respect to the variables K and λ be zero, we obtain the following euation set: ( l l ) λ( P P ) = 0, + K + π ( K, K,, K ) P = 0. 2 Q (3.5) Eliminating the parameter λ we obtain the following recursive relation. K (P+ P )( l l) = (P P )( l l ) + K, (3.6) where =2,3,.Q-. This euation defines the relationship between the variables K and K. n particular, a guidance analytical solution can be obtained if K could be expressed as a simple function of K, say K = K. n general, however, we have to use a numerical algorithm to find the optimal solution We note that our algorithm solves the optimisation problem directly. This is in contrast to the simplified method used in [7,8,9], where the packets are classified into a number of categories, each was assigned with an average relative priority index (loss impact), and the optimal mapping from the categories to S classes was searched for. Our optimisation algorithm can be ideally applied to segments of a relatively small number of data packets, which represent a video bit stream of a short duration, say a few seconds. Here the reuirement of small number of packets is based on the consideration that sorting may be done uickly enough (near real-time). f the sorting and optimisation is done off-line, our algorithm can also be applied to a large number of packets. According to the works of ovrolis et al [3,3,4], for relative differentiated networks the normalised loss

4 rates, l / λ, can be kept eual for all classes where { λ, Q} is a set of parameters of the relative iffserv network. n this case, the relationship (3.6) becomes, (P+ P )( λ λ) K = K (3.7) (PP )( λ λ+ ) Therefore, the parameters λ (instead of l ), together with the unit price parameters P can be used in the algorithm. From E.(3.6) we can derive a very interesting reuirement for the iffserv parameters. Since the packets have been sorted into ascending order, we have K K. Therefore the following condition must be satisfied, (P+ P )( l l). (3.8) (P P )( ) + l l A sufficient (but not necessary) condition for this to be satisfied is that both the prices and the loss rates are convex functions of he S number. That is: P ( P + P+ )/2, and l ( l + l+ )/2. For example, prices can be a linear or uadratic 2 function ( P = α+ β or P = α + β ) and the loss rates can be either linearly or inversely proportional to the class number (i.e., l = δ µ or l = µ /, where µ and δ are suitable positive parameters). The distribution of the packets among the S levels depends strongly on the iffserv parameters P and l. For example, if both functions, then we have P and l are linear K K = which implies that K = K. n such a case, K packets are mapped to the lowest S class, and the remaining ( N K ) packets are mapped to the highest S class, where K can be expressed as E.(3.3). No intermediate S class needs to be used in such a scenario. 4. Experimental Tests For the following experiments, we used the Microsoft reference MPEG-4 codec software (version fdam -2.3) with necessary modifications for our tests. We encoded a 0-second CF-formatted Foreman seuence with a frame rate of 0 frames/s. The TM5 rate control algorithm was used to produce a constant bit rate of 320 kb/s. An -frame was enforced every second (0 frames). Video packet size was set to be 500 bytes. At this set of encoding parameters, the resulting bit stream consists of 829 video packets. We computed the loss impact of each packet using the three-factor method of Shin et al [7] and the method proposed in section 2, respectively. The video packets were sorted according to their loss impact, and an eual-cost QoS mapping was considered: the first 00 packets that contain VOP headers and have the highest loss impacts are assigned the highest priority, the next 233 packets medium priority, and the remaining 496 packets lowest priority. For comparison, we also considered a content-blind QoS mapping: randomly pick up 00 packets as highest priority, 233 packets as medium and 496 packets as lowest priority. We used the Network Simulator [24] to simulate a iffserv network. The network topology consists of two edge routers and one core router [9]. The routers were configured to have one physical ueue with three virtual ueues. Each virtual ueue had a different drop precedence achieved through the use of the Random Early etection (RE) algorithm. The Low priority ueue corresponds to a best effort P service. Background traffic comprised of 3 UP flows, 5 ftp connections, and 3 bursty traffic flows. Each of the traffic flows starts randomly within the time interval [0,0). Their starting times are determined by a pseudorandom number generator seed, which is set at the beginning of a simulation. The background traffic runs for at least 0 seconds before video streaming commences. n fact the video starts at a random time in the interval (20,30). n this way, the simulations are repeatable since all the random variables are determined from the initial seed. Keeping the network configurations and background traffic unchanged, but varying the initial random number generator seed, we sent each prioritised packet stream through the iffserv network 50 times. Statistically averaged results are presented in Table. The observed average packet loss rate is about 7%. The content-blind method yields the lowest PSNR uality and it suffers frame losses because of lost video packets containing VOP headers. Both the proposed method and the three factor method can protect VOP headers effectively as they sent all the VOP headers via the highest priority class. However, The proposed method outperforms the three-factor method by 0.63 db in PSNR. We also did a set of simulations with less background traffics so that the average packet loss rate was lower (about.0%). n this case we found that our method was about 0.2 db better than the three-factor method of Ref.[7]. To examine the effectiveness of our optimal QoS mapping algorithm (Section 4), we simulated a network with three S classes (Q=3) with a linear

5 pricing model, and the loss rates for the low, medium, and high priority classes are 0%, 2.5%, and 0%, respectively. The packets loss impacts were calculated with the method in Section 2. And under a given budget constraint, the packets were prioritised by the new QoS mapping algorithm and the previous category-based QoS mapping algorithm [7], respectively. n each case, the optimisation was done over the entire packet stream representing 0 seconds of video seuence. Figures and 2 show that the new QoS mapping algorithm performs better than the algorithm proposed by Shin et al [7]. n particular, with the new algorithm, the optimal mapping is very close to the exact optimal mapping obtained through exhaustive search and the video uality increases smoothly with the budget (Here for Q=3 and 829 packets, the optimisation problem can still be solved through exhaustive search. However, when Q>3 the computing time becomes too long for exhaustive search to be practically useful.). n contrast, the Shin s optimisation algorithm cannot minimize the loss impact effectively. As a conseuence, the objective uality obtained with Shin s method [7] is poorer than with the new method. 5. Concluding Remarks We have addressed two important issues in streaming video over differentiated services networks. First, the loss impact of a video packet has been analysed and a simple method to measure the video uality degradation due to the packet loss has been proposed. The new method uses standard video packets produced by an encoder and does not involve modifications of the bit stream syntax. We have shown that packet prioritisation based on the proposed loss impact achieves better uality than the content-blind way of prioritisation, and outperforms a previous three-factor prioritisation method. We have also studied the optimal QoS mapping from the video packets to a set of S levels. Our analysis has shown clearly how the optimal QoS mapping depends on the S network parameters, namely the unit price and the loss rate of each S level. n particular, we have shown that in order to have all S levels used in the optimal QoS mapping, the unit prices and loss rates must satisfy certain condition as given in E.(3.8). This result may be useful for differentiated network service providers in determining their service parameters such as prices and relative loss rate parameters. Our simulation has shown that the present QoS mapping algorithm is more accurate and produce better objective video uality than the algorithm proposed in previous works. Table. The objective uality of streaming MPEG-4 video over a S network simulated by the NS [24]. Method Number of Frames decoded PSNR (db) Content-blind Method of [7] Proposed Without error Normalized Loss mpact Exhaustive Search New Shin et al [7] Price Constraint/Packet Fig.. The normalized minimal Loss mpacts achieved with three different methods. Note that the new method can produce virtually the same results as the exhaustive search method (asterisks inside the suares). PSNR (db) New Shin et al [7] Price Constraint/Packet Fig.2. The average PSNR values of decoded video seuences (50 samples) versus the budget per packet, showing that the present optimal QoS mapping algorithm (suares) performs better than the method of Ref [7] (triangles). References [] R. Braden,. Clark, and S. Shenker, ntegrated Services in the nternet Architecture: an Overview, ETF RFC-633, 994. [2] K. Nichols, S. Blake, F. Baker, and. Black, efinition of the ifferentiated Services Field in the Pv4 and Pv6 Headers, ETF RFC-2474, ec. 998; S. Blake,. Black, M. Carlson, E. avies, Z. Wang, and W. Weiss, An Architecture for

6 ifferentiated Services, ETF RFC-2475, ec [3] C. ovrolis and P. Ramanathan, A Case for Relative ifferentiated Services and Proportional ifferentiation Model, EEE Network, vol 3 pp.2-0, Sept/Oct, 999. [4] J. Heinanen, T. Finland, R. Guerin, A Single Rate Three Color Marker, ETF RFC-2697, 999. [5] J. Heinanen, T. Finland, and R. Guerin, A Two Rate Three Color Marker, ETF RFC-2698, 999. [6] W. Fang, N. Seddigh, and B. Nandy, A Time Sliding Window Three Colour Marker, ETF RFC-2859, [7] V. Jacobson, K. Nichols, K. Poduri, An Expedited Forwarding PHB, ETF RFC-2598, 999 [8] J. Heinanen, F. Baker, W. Weiss, J. Wroclawski, Assured Forwarding PHB Group, RFC 2597, ETF, June 999. [9].. Clark, and W. Fang, Explicit Allocation of Best-Effort Packet elivery Service, EEE/ACM transactions on networking, Vol.6, August 998, pp [0] B. Teitelbaum, S. Hares, L. unn, R. Neilson, V. Narayan, nternet2 QBone: building a testbed for differentiated services, EEE Network (September/October 999) pp.8-6. See also the nternet2 website: [] W.C. Feng,.. Kandlur,. Saha, and K.G. Shin, Adaptive packet marking for maintaining end-to-end throughput in a differentiated services nternet, EEE/ACM Transactions on Networking, Vol. 7, pp , October 999 [2] F. Wang, P. Mohapatra, S. Mukherjee,. Bushmitch, An efficient bandwidth management scheme for real-time nternet applications, Computer Communications [3] C. ovrolis,. Stiliadis, and P. Ramanathan, Proportional differentiated services: delay differentiation and packet scheduling, EEE/ACM Trans on Networking, Vol 0, 2-26, Rate differentiation and packet dropping, in EEE/FP nt Workshop Quality of Service (WQoS) June 2000, pp [5] M. K.H. Leung, J.C.S. Lui, and. K.Y. Yau, Adaptive proportional delay differentiated services: characterization and performance evaluation, EEE/ACM transaction on networking, Vol 0 (200), p.80. [6] Y. T. How,.Wu, B. Li, T. Hamada,. Ahmad, H. Jonathan Chao, A differentiated services architecture for multimedia streaming in next generation nternet, Computer Networks 32 (2000) pp [7] J. Shin, J Kim, and C.-C. Jay Kuo, Quality-of- Service mapping mechanism for packet video in ifferentiated Services Network, EEE Trans on Multimedia, Vol. 3, No. 2 (200), pp [8] J.-G.Kim, J. Kim, J.Shin, and C.C.J.Kuo, Coordinated packet level protection employing corruption model for robust video transmission, Proc. Visual Communications and image processing, January 200. [9] J. Shin, J. G. Kim, J.W. Kim, and C.C.Jay Kuo, ynamic QoS Mapping Control for Streaming Video in Relative Service ifferentiatio Networks, pp [20] H-R Shao, W Zhu, Y-Q Zhang, User-Aware Object-based Video Transmission over the Next Generation nternet, Signal Processing: mage Communication, Vol (200). [2] T. Ahmed, G. Buridant, and A. Mehaoua, Encapsulation and marking of MPEG-4 video over P differentiated services, Proceedings of EEE Symposium on Computers and communications 200. Tunisia, pp [22] T. Ahmed, A. Mehaoua, and G. Buridant, mplementing MPEG-4 video on demand over P differentiated services, EEE Global Telecom conference records (200). PP [23] H.Yu,. Makrakis, and L. O.Barbosa, Experimental evaluation of MPEG-2 video over differentiated services P networks, Proceedings of EEE Pacific RM Conference on Communications, Computers, and Signal Processing, , 200. [24] UCB/LBNL/VNT, Network Simulator (NS). [4] C. ovrolis and P. Ramanathan, Proportional ifferentiated services, Part : Loss