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➡ A PROTOCOL WITH TRANSCODING TO SUPPORT QOS OVER INTERNET FOR MULTIMEDIA TRAFFIC Rajeev Kumar Department of Computer Science and Engineering Indian Institute of Technology Kharagpur – 721 302, India
[email protected] ABSTRACT The growth of the Internet has brought with it a tremendous volume of multimedia traffic, which is bursty in nature. Providing a required QoS as well as modeling multimedia traffic has been a challenging task. In this work, we transcode multimedia data to cater for low bandwidth availability and different end-user requirements. We propose a protocol architecture which has been developed by the amalgamation of well-known components and that would provide guaranteed multimedia communication over the Internet. We model multimedia traffic using the M/Pareto distribution in an attempt to represent realistic traffic pattern. We use semantics of multimedia data-streams for transcoding to avoid network congestion and to ensure optimal use of network resources. The impact of transcoding the multimedia data to suit it to the network load and the end user requirements is also studied. The simulation results are presented and compared. 1. INTRODUCTION Multimedia applications require different type of services and need application specific Quality of Service (QoS) to perform to the required standards. Thus, Quality of Service (QoS) gains importance for multimedia and real time applications. Many of multimedia applications are sensitive to the QoS rendered to it by the underlying network architecture, and their performance degrades rapidly in the event of a failure to provide the required QoS. However, today’s Internet architecture does not support the notion of QoS, and simply provides the connectivity. Internet, thus, offers a point-to-point best effort service such that all packets receive the same quality of service irrespective of their QoS needs. Therefore, the Internet Engineering Task Force (IETF) proposed many service models and mechanisms for providing some level of QoS over the unpredictable IP network, such as Integrated Services (IntServ)/Resource Reservation Protocol (RSVP) model, Differentiated Services (DiffServ) model, multiprotocol label switching (MPLS), traffic engineering, constraintbased routing and others [1-2]. IntServ model aims to provide an absolute guarantee of QoS for each flow across the network and monitors that each flow does not violate its reservation of resources. Each router maintains state information for each flow and does a significant amount of processing for each flow. Thus, IntServ model is not
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scalable. Also it does not suit to short-lived flows due to significant signaling and processing overheads. Consequently, DiffServ model which maintains aggregated-flow information, was proposed as an alternative model for providing QoS over the Internet that overcomes the shortcomings of the IntServ model. However, DiffServ model adds some measure of unpredictability to the network and does not attempt to guarantee any level of service; DiffServ simply provides a relative QoS for an aggregation versus another aggregation. Thus, such a mechanism is unable to meet the varying delay-constraints imposed by different applications and especially under heavy loadconditions. In this context, transcoding is a technique employed by network proxies and servers to dynamically customize multimedia objects for prevailing network load/conditions and individual client characteristics. Transcoding can be performed along a number of axes; the specific transcoding technique used depends on the type of multimedia objects, network load and different types of resolutions/parameters provided by the client device to present the media objects [3]. Achieving a desired temporal-spatial rate along with a desired perceptual quality of the media objects within the available compute resources is one among the many issues involved in transcoding. In this work, we use transcoding to guarantee an achievable QoS for multimedia traffic under varying network load conditions. In this paper, we explore the possible transcoding mechanism/algorithms, study their impact on the network performance, and compare the QoS parameters with and without transcoding. We propose a modified protocol which works on the network to guarantee QoS for multimedia communication over the Internet. The proposed method is an amalgamation of different well known highly optimized sub-mechanisms. We stress the primary aim of this work is not to decide upon the exact transcoding framework, but rather to study the impact and use of transcoding to offer a superior QoS mechanism to multimedia traffic.
2. RELATED WORK Modeling multimedia traffic has been a challenging problem. Unlike ordinary network traffic which can be modeled with the use of the Poisson distribution function, multimedia traffic has different characteristics. Researchers observed the phenomenon of self-similarity in multimedia and web traffic and discussed the properties of Multimedia traffic. They gave a qualitative analysis
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➡ for Internet traffic and proposed a theoretical model of multimedia traffic. It is researched by many that this can be appropriately modeled using the M/Pareto distribution [4-5]. Neame & Zukerman [6] outlined a detailed methodology of generating self-similar traffic using the M/Pareto model. In this paper, we use their work as the primary reference to generate the multimedia source traffic. The other facet of the problem under consideration is an effective architecture to provide the desired QoS to a variety of applications having divergent needs, e.g., [7]. Many studies have been done yet it remains an open problem. For example, Sarangan et al. [8] incorporated distributed QoS for multimedia traffic and applied preferential routing to IP routers. Others discussed IP router architecture and the various algorithms used for routing. Many other studies were done for detailed insight into the working and functionalities of RSVP, congestion control mechanism for streaming video, and other aspects. There are many more studies in literature in reference to obtain the desired QoS for multimedia traffic too. However, the provision of hard QoS guarantee is often not possible on the Internet due to the presence of numerous traffic sources of varied origin, their volume and typical characteristics. In this context, transcoding is emerging as a possible solution to achieve efficient and reliable multimedia communication by modeling the multimedia data according to network constraints and end-user requirements. There are many approaches to transcoding a multimedia object, e.g., [3] and [9]. For example, Mohan et al. [3] discussed the key aspects of transcoding multimedia content for universal access across the Internet and for heterogeneous client devices. Chandra et al. [10] put forward a detailed analysis of quality aware transcoding to provide differentiated web services and feasibility of transcoding to enhance the performance of web servers. They aimed at devising an efficient transcoding framework, which would provide the correct balance between computational overhead and reduction in the resolution of the multimedia object. Knutsson et al. [11] discussed a server based transcoding framework. Cucchiara et al. used semantics for video transcoding [12]. Liu and Kuo [13] studied in detail transcoding the video content for adaptable delivery and the computational requirements of different types of transcoding. There are many other studies regarding many other aspects too However, to our knowledge, no published work is available which combines the communication protocols, architecture and transcoding, and analyzes the quantitative advantages over the conventional approaches. In this paper, we appropriately model the traffic and propose the architecture/protocol. We simulate the traffic, which is quite close to real multimedia traffic, and collect results of the performance of the proposed architecture with and without transcoding.
order as compared to O(n2) required for the other models. In this work, we use the M/Pareto model [6] for generating multimedia traffic. The M/Pareto model belongs to the M/G/∝ group of processes [6]. The inter arrival time between traffic bursts is derived from a Poisson distribution. The total burst size is given by a Pareto distribution. While the inter arrival time within a burst is kept constant and can be modified to fit to the parameters of the traffic to be modeled. A larger frame is divided into packets for transmission. The inter arrival time within a burst dictates the time difference between the transmission of each such packet. Pareto distribution has the following probability density function:
3. MODELING MULTMEDIA TRAFFIC
We modeled the compressed video transmission with respect to the following parameters: max /average frame size, frame rate, spatial and temporal resolutions, the sampling formats and the application. We use M/Pareto distribution with three different mean values to generate the frame sizes for each of I, P and B frames in order to resemble to the GOP structure of a compressed MPEG video. For a self-similar traffic, we generated the multimedia traffic with many values of the shape parameter and used the Pareto shape parameter of 1.9 for generating the traffic for the work reported in this paper. The
A notable character of Multimedia traffic is that it is bursty in nature and shows self-similarity. So, application of the conventional technique to modeling multimedia traffic was not very successful. It has been found that the M/Pareto model is better suitable to model traffic, which has close resemblance with real multimedia traffic. Apart from this, the M/Pareto model is faster computationally as compared to the other models. The number of computations required for the M/Pareto is of O(n)
P[ X > x] ~ x −α for x ≥ 1 P( x ) =
ak a x a +1
,
Where, α is a shape parameter, k is minimum value of x.
Mean =
ak a −1
The Pareto distribution is generated by,
X =
k U −a
U is a uniformly distributed random variable in the range [0, 1]. It is expected that majority of multimedia objects being transacted over the Internet in near future will overshadow any other media of communication; video data may be the primary source of multimedia traffic. With this assessment, we have generated video-data using the M/Pareto distribution for the simulation. We assume the inter frame arrival time constant for the traffic generated because video has a constant frame rate.
Fig. 1: Generated data from Pareto distribution.
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➡ frames are generated at an interval of time, which corresponds to the frame rate of the video clip. The inter-arrival time within a burst is kept constant and is fixed to the time required to service a frame. A plot of a random sample generated from the Pareto distribution over a long range of time is shown in Figure 1. On plotting the distribution taken from each of I, P and B frames, we observed that the frame-sizes are according to the Pareto distribution. However, we deliberately made the data distribution not continuous at the ends as expected of a Pareto curve; this is done so that the generated data resembles to a compressed video clip. We partitioned each of I, P and B frames into fixed sized data packets for processing and subsequent transmission, and thus generate a continuous stream of packets.
larger quantization step size, and thus reduces data-rate as well as the quality. Such technique is of very low complexity but of poor quality. On the other extreme, a transcoder may have full decoder followed by a full encoder and performs in pixel domain. Such a trnascoder is of very high complexity and is not suitable for on-line processing. A practical transcoder re-uses the motion computation and results in high quality along with low complexity [13]. Thus, semantic information may be used for enhancing the perceptual quality as well as SNR [12]. We have tested all the three approaches for this work. However, we have included results, in the following section, obtained by the transcoder which re-uses motion computation.
6. RESULTS AND DISCUSSION 4. PROPOSED PROTOCOL ARCHITECTURE We use the following main components to provide the required QoS - Queuing/Scheduling, Resource Provisioning/Resource Reservation, Congestion Avoidance and Routing. For queuing/scheduling we use a Priority Queuing mechanism, which gives the multimedia traffic the highest priority over other traffic in the network. We use RSVP to set up reservations for network resources. The Weighted Random Early Detection (WRED) is used as the congestion avoidance algorithm. We propose the routers to use OSPF as their routing algorithm. The proposed protocol architecture has been formed by combining the features of DiffServ (e.g., PQ) and IntServ (e.g., RSVP) to overcome this shortcoming of each of the models. In brief, the proposed protocol architecture has the priority field and utilization of TOS field in IP header, priority queuing, RSVP, WRED, a distributed QoS routing algorithm, OSPF and Transcoding. The transcoding is described in the following section.
5. TRANSCODED TRAFFIC We perform transcoding for the following two broad classification – (i) transcoding to avoid network congestion and cater to low bandwidth, and (ii) transcoding to suit end users with specific support as regards to processing power and/or the display resolutions. As the network load increases and the presence of numerous other multimedia traffic makes the network congested, the best way to make amends for unavailable bandwidth is to lower the multimedia object quality. The simulator we use has a parameter that gives a measure of the network load. We have used this parameter to determine as and when transcoding is required and trigger the same accordingly. However in real network situations, statistical information about the network performance and load at different times may be used to trigger transcoding. The use of a signaling protocol may be employed to get information about the network load and then trigger transcoding. The transcoding of video have various approaches [9] and [13]. Spatial transcoding reduces the frame size while temporal decreases the frame rate by dropping less significant frames. Color transcoding reduces the data size by decreasing the color depth used. Frame size can be reduced by lowering the quality factor based on the information content. In extreme cases, a frame may optionally be dropped or communicated with a reduced rate. For compressed video, a simple video transcoder requantizes the DCT coefficients of the input bit-stream by a
We perform simulations for varying reservation of multimedia traffic. Each intermediate nodes assumes the presence of 20 cross traffic generators. Each of these cross traffic generators generates traffic, which is categorized into three types: multimedia, control and the miscellaneous. We assign the highest priority to multimedia traffic followed by control and other traffic. The control and the miscellaneous traffic are generated using the Poisson distribution. The performance metric is based on the following QoS parameters: end-to-end delay, packet loss probability and delay jitter. These parameters are evaluated for an increasing network load. The network load dictates the amount of cross-traffic being generated at the intermediate nodes. We compare the performance of the proposed protocol with and without transcoding. We shall include other details of the simulator and the architecture in the final paper. The source traffic generated using the Pareto distribution is used in our network simulator to find the behavior of the proposed protocol with and without transcoding. We simulate the QoS specific results for the following cases: Communication using the proposed protocol, Communication using the proposed protocol with transcoding based on network load, and Communication using the proposed protocol with transcoding based on end-user requirements. Transcoding is done to reduce network load and (possibly) maximize network utilization (in terms of number of flows) along with possible reduction in end-to-end delay, loss and/or delay jitter for the multimedia packets. The main effect of transcoding with respect to simulation studies is data reduction at the cost of additional processing time which varies from packet to packet. (The additional processing time depends on many factors.) In the performance study we have transcoded the multimedia data based on two broad aspects. One, when the network load increases, data streams are transcoded to reduce end-to-end delay, loss, and delay jitter. Two, when the end user has limitations with regard to processing power or display capabilities, the multimedia data is sent with reduced volume even when the network load is not appreciable. In both these cases the qualitative QoS suffers, it may not be perceptually appreciable by the end-user. In this work, we assume that transcoding re-uses motion vector computations. We include a few results for end-to-end delay and delayjitter under varying load conditions and obtained with different set of protocols. In Figure 2, the end-to-end delay though similar
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Fig. 2: Variation of end-to-end delay with network load. The delay-jitter for different packets on arrival is depicted in Figure 3; the bounded delay variance is not significantly reduced for larger number of packets with the proposed protocol along with the transcoding. It is due to the reason that in proxybased architecture, the additional processing-time involved for transcoding is frame/packet dependent. This may further be improved by shifting some of the computations to off-line.
communication. We were however unable to exhibit hard QoS as the network simulator assumes the presence of multimedia cross traffic which has a priority equal to that of the host traffic. As a solution to this, we included a transcoding layer to the protocol by which the host multimedia traffic is adapted to the network load. Secondly we also proposed to adapt the host traffic to the capabilities of the client. Simulation studies show that some of the QoS parameters, e.g., loss probability due to congestion, are always improved under varying condition of network load. End-to-end delays are reduced; the quantum of reduction depends on transcoding level used and the transcoding framework. However, in proxy-based transcoding, delay-jitter may not improve; it is due to the varying processing involved for different frames. This is dependent upon the transcoding level as well as the transcoding architecture. An estimation of the parameters needed to transcode the data-steams is an area of further investigation.
7. ACKNOWLEDEGMENTS Author acknowledges the support received from the Ministry of Human Resource Development, Government of India Project for pursuing the research work.
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Fig 3: Delay Jitter at 75% reservation for multimedia traffic. Summarizing, under lighter network load, there is not much improvement in QoS – end-to-end delay, loss and jitter – parameters. This is justifiable. However, there is significant improvement under heavy load conditions with the use of proposed protocol and the transcoding scheme. Eventually, this justifies triggering the transcoding mechanism for heavier loads to ensure meeting the quantified QoS parameters at the cost of reduced information which may not be perceptually loss-free.
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7. CONCLUSIONS [11]
In this work we have used the M/Pareto model due to its ability to generate traffic which is bursty and shows self-similarity, and generated the source traffic to closely resemble real multimedia traffic. Then we communicated the traffic over an architecture/protocols which is formed by the amalgamation of individual components selectively taken from the IntServ and DiffServ models. We compared the performance of our protocol in providing guaranteed QoS as compared to best effort
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