GROUP-2 Amit Kumar(55115890) Princepreet Bhatti(31116116) Scene-Change Aware Dynamic Bandwidth Allocation for Real-Time VBR Video Transmission Over IEEE 802.15.3 Wireless Home Networks GROUP-2 Amit Kumar(55115890) Princepreet Bhatti(31116116)

Abstract The VSSNLMS predictor used in this paper was found to be superior to previous LMS-type predictors in performance. The proposed scheme showed better performance in channel utilization, buffer usage, and packet loss. Previous method: the adaptive least-mean square (LMS) algorithm with fixed step size was applied to predict channel time requirements due to its simplicity and relatively good performance. The drawback was that the performance might degrade when scene changes occurred. Here, the variable step-size LMS algorithm was modified and apply it as the predictor (VSSNLMS) so that the prediction errors on scene changes can be effectively reduced. Using the prediction results of VSSNLMS, a dynamic bandwidth allocation scheme was proposed that is scene-change aware and can guarantee the delay bound of real-time VBR videos.

Introduction The new IEEE 802.15.3 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, the IEEE 802.15.3 standard also supports all functionalities needed for reliable QoS. The IEEE 802.15.3 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.

DYNAMIC BANDWIDTH ALLOCATION IN IEEE 802.15.3 Dynamic bandwidth allocation in an IEEE 802.15.3 piconet involves isochronous stream management and asynchronous channel time management, where the former was designed for supporting video streams. This paper adopts the isochronous stream management as the dynamic bandwidth allocation mechanism for VBR video transmission. The following figure (Fig. 1), we further explain the isochronous stream management with an example, where a video conference DEV is required to transmit a VBR video to a TV.

DYNAMIC BANDWIDTH ALLOCATION IN IEEE 802.15.3 For a real-time VBR video, a DEV needs to predict the bandwidth requirement of the next superframe, because it is not able to determine the actual bandwidth requirement in advance. Each superframe contains three parts: Beacon, contention access period (CAP), and channel time-allocation period (CTAP). If the DEV overpredicts its bandwidth requirement, the channel utilization is lowered. On the other hand, an underprediction of bandwidth requirement will incur more buffer, more delay and more packet loss. Therefore, an accurate VBR video traffic predictor for a DEV is crucial in order to enhance channel utilization and guarantee the QoS requirements of real-time VBR videos. Previous work can be classified into three categories: model-type predictor, LMS- type predictor with fixed step size, and LMS-type predictor with variable step size

Model-Type Predictor Model-type prediction is not suitable for real-time VBR videos to enhance the performance of model-type prediction, the parameters should be estimated accurately, which requires a large amount of traffic data. Overall, the application of model-type predictor to the dynamic bandwidth allocation for IEEE 802.15.3 involves several problems. First, it is difficult to implement a model-type predictor in a low-cost IEEE 802.15.3 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. In earlier studies, the NLMS algorithm augmented with a scene change indicator was proposed for real-time VBR MPEG video prediction. This paper uses SCINLMS to denote this NLMS algorithm. 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.

Drawbacks of Fixed step size - LMS Two drawbacks of a fixed step-size LMS-type predictor for predicting real- time VBR videos. Difficult to determine the order and step size of the predictor for different VBR videos in order to achieve optimal performance. The predictor does not have smaller mis-adjustment 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. Determining the order and step size of an adaptive LMS-type predictor is difficult provided prior statistics of VBR videos are not available.

VSSLMS (Variable step size - LMS) 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 difficult 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 802.15.3 devices, but has a satisfying performance for predicting VBR videos.

Modified Variable Step-Size LMS The problem analysis in paper shows that prediction errors caused by scene changes can be relieved effectively while the proper value of step size can be automatically adjusted for different VBR videos by an online manner. This can be achieved by modifying the VSSLMS algorithm and applying it to be our VBR video predictor. VSSNLMS

SCADBA (scene-change aware dynamic bandwidth allocation) The SCADBA scheme employs the prediction results of VSSNLMS to request the bandwidth requirement of real-time VBR videos for the next superframe. In order to achieve a more reliable transmission, a management CTA – channel time allocation (MCTA) is allowed to be used, instead of a CAP(Contention access period). MCTAs, which are a kind of CTAs, are used only for communication between DEVs and the PNC (piconet coordinator). 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 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.

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. 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.

C. 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.

V. CONCLUSION Firstly, this paper 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 The variable step-size LMS (VSSLMS) algorithm of a previous study [12] was modified so that the modified algorithm could effectively reduce the prediction errors on scene changes. The modified algorithm was applied as the VBR video predictor (VSSNLMS). The performance of the SCADBA scheme was evaluated by means of channel utilization, buffer usage, and packet loss.

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