Scene-Change Aware Dynamic Bandwidth Allocation for Real-Time VBR Video Transmission Over IEEE Wireless Home Networks Amarjit Mayank

Abstract Abstract—IEEE , an emerging wireless technology, was designed to provide high-quality multimedia services at home. So we consider Dynamic bandwidth allocation for a multimedia connection for better channel utilization, less buffer, less delay for variable bitrate multimedia connection. In order to do that we modify the variable step-size LMS algorithm and apply it as our predictor (VSSNLMS) so that the prediction errors on scene changes can be effectively reduced. Using the prediction results of VSSNLMS, we propose a dynamic bandwidth allocation scheme that is scene-change aware and can guarantee the delay bound of real-time VBR videos.

Introduction For Home-Networks we wish to achieve a wire like connectivity for multimedia streaming on a wireless network. The new IEEE standard for high-rate wireless personal area networks (WPANs) is an emerging wireless technology that combines low cost and low power with high data rates and robust quality of service (QoS). In addition to high data rates, it also supports all functionalities needed for reliable QoS. This standard’s MAC protocol uses time-division multiple access (TDMA) to allocate channel time among devices, in order to prevent conflicts, and it allocates new channel time for a connection only when enough bandwidth is available that uses piconet- contains a number of independent data devices (DEVs) that are allowed to exchange frames directly with each other, which is a wireless ad hoc data communications system in essence.

We use the concept of master and the others are slaves. The PNC is also responsible for admission control and channel-time allocation. Timing for a piconet is realized by superframes. Each superframe contains three parts: Beacon, contention access period (CAP), and channel time-allocation period (CTAP). It was indicated in that constant bit rate (CBR) encoding may reduce the picture quality, particularly at scene changes or intervals with significant detail or motion, while VBR encoding can offer higher picture quality and greater opportunity for statistical multiplexing gains. Since the bandwidth in IEEE WPANs can be allocated on- demand, smoothing buffers can be removed and hence coding kernels are directly connected to networks.

Since the peak-to-mean ratio of a VBR video is usually high, requesting CBR channel allocation that satisfies the peak-rate requirement often leads to low channel utilization. On the other hand, requesting CBR channel allocation with a nonpeak rate may cause more delay, more buffer and more packet loss. However, the channel-time requirement of a real-time VBR video for successive superframe is unknown. Therefore, it is crucial for DEVs to have the ability to predict the channel time requirement of subsequent superframes in order to support real-time VBR video transmission. By allocating bandwidth of the predicted amount, only the errors of prediction need to be buffered. Thus, higher channel utilization, less buffer, and less delay can be achieved as long as the prediction is accurate enough. This paper proposes a scheme that modifies the variable step-size LMS (VSSLMS) algorithm to effectively relieve the prediction errors on scene changes.

Dynamic bandwidth allocation in IEEE – related work Dynamic bandwidth allocation in an IEEE piconet involves isochronous stream management and asynchronous channel time management. This paper adopts the isochronous stream management as the dynamic bandwidth allocation mechanism for VBR video transmission. Model Type Predictor and it’s Challenges- Model-type predictor deals with the development of stochastic source models and adopts these models to predict. For statistical model-type predictor, the fractional autoregressive integrated moving average (F-ARIMA) model [15] is the most popular one. F-ARIMA is a self-similar model that has the ability to capture both the SRD and LRD characteristics.

Model-Type Predictor To enhance the performance of model-type prediction, the parameters should be estimated accurately, which requires a large amount of traffic data. Hence, model-type prediction is not suitable for real-time VBR videos. Overall, the application of model-type predictor to the dynamic bandwidth allocation for IEEE involves several problems. First, it is difficult to implement a model-type predictor in a low-cost IEEE device because the computation required for model-type predictor is heavy. Second,modeling VBR videos is a great challenge due to its complex traffic characteristics. Third, prior knowledge of the autocorrelation structure of VBR videos is required. Thus, model- type predictor is not suitable for online prediction and for real-time VBR video transmission.

LMS-Type Predictor With Fixed Step Size An adaptive LMS-type predictor does not require any prior knowledge of the video statistics, and it does not assume video contents to be stationary The prediction of a linear LMS predictor iteratively executes two steps. The first step is to calculate the prediction result by a linear combination of the current and previous values. The second step is to execute an adaptive process that involves the automatic adjustment of the parameters of the LMS predictor in accordance with the estimation error. The combination of the two steps constitutes a feedback loop. The generated prediction results are approaching to the optimization step by step.

LMS-Type Predictor With Variable Step Size An adaptive LMS-type predictor with fixed step size is expected to produce large errors on scene changes. The NLMS algorithm augmented with a scene change indicator was proposed for real-time VBR MPEG video prediction. Intuitively, SCINLMS increases the value of whenever a scene change is detected. To speak more concretely, SCINLMS dynamically sets the value of in (4) to be or, where and STEP_JUMP are two constants and are determined according to the video characteristics. The SCINLMS predictor uses a scene change indicator to detect scene changes for I frames. The scene change indicator cannot be used for P frames and B frames, because their statistical characteristics are different from that of I frames

SCENE-CHANGE AWARE DYNAMIC BANDWIDTH-ALLOCATION SCHEME Problem Analysis for LMS-Type Predictors With Fixed Step Size There are two drawbacks of a fixed step-size LMS-type predictor for predicting real-time VBR videos. One is that it is diffi- cult to determine the order and step size of the predictor for different VBR videos in order to achieve optimal performance. The other is that the predictor does not have smaller misadjustment and better performance for handling scene changes simultaneously. Since the order and step size of a fixed step-size NLMS predictor have to be determined before prediction,it is difficult for the predictor to operate well for different VBR videos. According to the above discussion, determining the order and step size of an adaptive LMS-type predictor is difficult provided prior statistics of VBR videos are not available. However, the statistics of most VBR videos are not available when the predictor starts to operate. Since the order and step size of the predictor have to be determined in advance, the performance may be inferior

Problem Analysis for LMS-Type Predictors With Fixed Step Size VSSLMS adjusts the step size dynamically according to the squares of prediction errors In VSSLMS the step size is updated dynamically according to the following recursive equation: VSSNLMS is adaptive to rapid traffic variation while scene changes occur. Rather than using the fixed step-size adaptive LMS-type predictor and the SCINLMS, which is dif- ficult to determine in advance the optimal parameters for different VBR video traffic, VSSNLMS adjusts its step size automatically for the statistics of different VBR video traffic. The computational complexity of VSSNLMS is also low. Therefore, VSSNLMS not only meets the low-cost requirement of IEEE devices, but has a satisfying performance for predicting VBR videos.

SCADBA The SCADBA scheme employs the prediction results of VSSNLMS to request the bandwidth requirement of real-time VBR videos for the next superframe. Furthermore, in order to achieve a more reliable transmission, a management CTA (MCTA) is allowed to be used, instead of a CAP. MCTAs, which are a kind of CTAs, are used only for communication between DEVs and the PNC. The predicted bandwidth requirements may be overestimated or underestimated. If overestimated, the arriving packets are all transmitted. If underestimated, some of the arriving packets are queued, which will cause longer packet delay. Moreover, if the capacity of the queue is not enough, packet loss will occur. The SCADBA scheme attempts to send out the queued packets so as to reduce the possibility of queue over- flow. The SCADBA scheme is thus realized as follows:

SIMULATION RESULTS Prediction Error (Comparisons) MPEG-4 video traces used in our simulation are from [21]. There are three types of frames for MPEG-4: intra-frame (I-frame), inter-frame (P-frame) and bidirectional-frame (B-frame). They constitute so called groups of pictures (GoPs). One GoP is the sequence of frames from an I-frame to (but not including) the next I-frame. A typical GoP pattern contains three P-frames and two B-frames interleaved between every two adjacent P-frames. An I-frame subsequence, which uses intra-coding without reference to other frames, varies very rapidly with scene changes. The bit rate for a P-frame subsequence increases rapidly at scene changes, whereas the traffic inside a scene is smooth. Since a B-frame adopts a bidirectional predictive coding scheme, B-frames can be accurately forecasted using an LMS predictor. A DEV, denoted by DEV1, is required to transmit a VBR video to another DEV, denoted by DEV2, within the same piconet. During the transmission, DEV1 modifies its channel time requirement to the PNC according to (9) for the next superframe The length of a superframe ranges from 0 ms to ms in IEEE WPANs. Video frames used in our simulation are generated at intervals of 40 ms by the MPEG-4 encoder. However, too small a length of a superframe may induce excessive power consumption. Hence, we set the length of a superframe to be 30 ms

Channel Utilization (Comparisons) In practice, the peak rate and the average bandwidth are not available to a real-time VBR video in advance. They are used only for the purpose of comparison. The SCADBA scheme has the best channel utilization, while the scheme of allocating peak rate has the worst channel utilization. Although the latter has the best video quality, almost a half of bandwidth is wasted. The cost is too high. SCADBA scheme has the best channel utilization, since the VSSNLMS predictor can increase accuracy of prediction, especially when scene changes occur.

Buffer Usage and Data Loss (Comparisons) The buffer usages of the SCADBA scheme, the scheme of allocating peak rate, and the scheme of allocating average bandwidth. The buffer usage of the SCADBA scheme is much better than that of the scheme of allocating average bandwidth. As expected, the buffer usage of the scheme of requesting peak rate is zero. The SCADBA scheme has the lowest average loss rate, which means that it can provide better video quality than the others for the same buffer capacity.

CONCLUSION We first showed that an adaptive LMS-type predictor with fixed step size might produce large errors on scene changes. Moreover, the proper step size must be predetermined, which is rather difficult provided prior statistics of real-time VBR videos are not available We modified the variable step-size LMS (VSSLMS) algorithm of [12] so that the modified algorithm could effectively reduce the prediction errors on scene changes. We applied the modified algorithm as our VBR video predictor (VSSNLMS) The performance of the SCADBA scheme was evaluated by means of channel utilization, buffer usage, and packet loss. Although the SCADBA scheme was designed for IEEE WPANs, it can be applied to RCBR networks such as wireless ATM, HiperLAN/2, and IEEE