Scalable Continuous Media Streaming Systems in Visual Studio .NET

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Bit-rate rmax rmin t Rate Controller t Transport Client Buffer Decoder Network
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Figure 8.1 Block diagram of the system model
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In practice, even with a transcoder, the video bit-rate may not be changed at any arbitrary time due to the structure of the coding algorithm (e.g., group of pictures). Thus, in the system model we assume video transcoding is performed in discrete video segments of xed playback duration of M seconds. The rate controller will then determine the target bit-rate for the next video segment based on estimation of the client s buffer occupancy. We denote the average bit-rate for the kth video segment by rk . The transcoded video segments are then transmitted to the client using TCP. Note that the server does not limit the transmission rate here and simply sends the transcoded video data as fast as TCP will allow. This ensures that available network bandwidth is fully utilized. Here, we assume that the total size of server buffer in between the media server application and the network (e.g., the buffer inside the socket library and TCP) is a known constant, denoted by Z . At the receiver, many existing video players will prefetch a certain amount of video data before starting playback to absorb the inevitable bandwidth uctuations. We denote the playback duration of the prefetched video data by B p seconds. Depending on the speci c player software, B p can be a xed value known to the server, or it can be con gurable by the users. If it is the latter case and the existing player software does not report this value to the server, the server will simply assume the worst case of no prefetch, i.e., B p = 0 second, in performing rate adaptation. In the case of client buffer starvation during video playback, it is assumed that playback will be paused until B p seconds worth of video data is again buffered at the client as depicted in Figure 8.2. Let G be the video frame rate. After the late arrival of frame i, the playback is paused and the client will have to rebuffer frames i to (i + B p G 1) before resuming playback. This rebuffering mechanism is common among commercial video player software such as Microsoft s Media Player and RealNetworks Video Player.
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8.4 Client Buffer Occupancy and Network Bandwidth Estimation
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The objective of the rate adaptation algorithm is to prevent playback starvation caused by client buffer under ow. To prevent buffer under ow, the server will need to estimate the available network bandwidth as well as the client buffer occupancy, in terms of seconds worth of video data.
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frame i arrives later than its expected playback time, rebuffering begins with playback paused. i 2 Frame arrival time : t Playback time : i 2 i 1 Rebuffering i i+1 i+2 i 1 i i+1 i+Bp G 1
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Figure 8.2 Rebuffering after buffer under ow at the client
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Estimation of the client buffer occupancy is performed every time the server completes submitting a video frame to the network transport for delivery. For example, if the common socket library is used, then this is equivalent to completing all send function calls for the video frame. Let ti be the completion time of submitting video frame i for transmission, and let f i be the index of the oldest frame (i.e., with the smallest index number) that has not yet been completely received by the client at time ti . Now as the server will submit data for transmission as fast as the transport allows, we can assume that the intermediate buffer at the server is always full, i.e., there are Z bytes of data accumulated awaiting transmission. Thus, we can estimate f i from
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where si is the size of frame i. Similarly, after frame i + 1 is submitted for transmission, we can compute f i + 1 using equation (8.1). Now if f i + 1 > f i , then we know that frame f i to frame f i + 1 1 must have arrived at the client during the time from ti to ti + 1 . Assume in this short interval the frames arrive at the client at a constant rate. Then we can estimate the arrival time of frame k, denoted by Tk , from Tk = ti + k + 1 fi (ti + 1 ti ) fi + 1 fi k [ f i , f i+1 1] (8.2)
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Note that in equation (8.2) we ignored network and processing delays in receiving ACKs from the client. Our simulations show that this does not have signi cant impact on the algorithm s performance. Knowing the arrival time of each video frame, we can then proceed to estimate the client buffer occupancy. First, we denote K as the rst frame buffered at the client during the initial prefetch period or the rebuffering period. K is initialized to 1 before streaming begins, and when rebuffering occurs, K is set to be the frame that arrives late, e.g., K = i in the example shown in Figure 8.2. Let Bi (in seconds of video data) be the client buffer occupancy when frame i arrives at the client. Then we can estimate the client buffer occupancy Bi according to the following rules:
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