An analytical model of a video transmission system is used to study the impact of its parameters. The optimal trade-off between error resilience using INTRA coding and the channel code rate is determined for given channel parameters by minimizing the expected mean square error at the decoder. It is shown that the results strongly depend on the burstiness of the channel.

Long-term memory prediction extends the spatial displacement vector utilized in hybrid video coding by a variable time delay permitting the use of more than one reference frame for motion compensation. This extension provides improved rate-distortion performance. However, motion compensation in combination with transmission errors leads to temporal error propagation that occurs when the reference frames at encoder and decoder differ. In this paper, we present a framework that incorporates an error estimate into rate-constrained motion estimation and mode decision. Experimental results with a Rayleigh fading channel show that long-term memory motion compensation significantly outperforms single-frame prediction.

A theoretical analysis of the overall mean squared error (MSE) in hybrid video coding is presented for the case of error prone transmission. Our model covers the complete transmission system including the rate-distortion performance of the video encoder, forward error correction, interleaving, and the effect of error concealment and interframe error propagation at the video decoder. The channel model used is a 2-state Markov model describing burst errors on the symbol level. Reed-Solomon codes are used for forward error correction. Extensive simulation results using an H.263 video codec are provided for verification. Using the model, the optimal tradeoff between INTRA and INTER coding as well as the optimal channel code rate can be determined for given channel parameters by minimizing the expected MSE at the decoder. The main focus of this paper is to show the accuracy of the derived analytical model and its applicability to the analysis and optimization of an entire video transmission system.

Long-term memory prediction extends the spatial displacement vector utilized in hybrid video coding by a variable time delay, permitting the use of more than one reference frame for motion compensation. This extension leads to improved rate-distortion performance. However, motion compensation in combination with transmission errors leads to temporal error propagation that occurs when the reference frames at the coder and decoder differ. In this paper, we present a framework that incorporates an estimated error into rate-constrained motion estimation and mode decision. Experimental results with a Rayleigh fading channel show that long-term memory prediction significantly outperforms the single-frame prediction H.263-based anchor. When a feedback channel is available, the decoder can inform the encoder about successful or unsuccessful transmission events by sending positive (ACK) or negative (NACK) acknowledgments. This information is utilized for updating the error estimates at the encoder. Similar concepts, such as the ACK and NACK mode known from the H.263 standard, are unified into a general framework providing superior transmission performance.

A theoretical analysis of the overall mean squared error (MSE) in hybrid video coding is presented for the case of error prone transmission. The derived model for interframe error propagation includes the effects of INTRA coding and spatial loop filtering and corresponds to simulation results very accurately. For a given target bit rate, only four parameters are necessary to describe the overall distortion behavior of the decoder. Using the model, the optimal trade-off between INTRA and INTER coding can be determined for a given packet loss probability by minimizing the expected MSE at the decoder.

In this paper a contour prediction scheme is presented. Its application in a region-based motion compensation algorithm is shown. To achieve optimal performance in a rate-distortion sense the predicted regions are optimized by using a Lagrangian cost function. By using contour prediction the bit rate for motion compensation can be decreased.