Introduction

Multimedia applications such as the wireless video phone have lead to the study of issues in error-resilient low bit-rate video transmission over noisy channels. Wireless links not only suffer from limited bandwidth problems but are also highly vulnerable to channel errors. Video compression standards like the H.261 alleviate the bandwidth problem to a certain extent. The H.261 standard [1] also known as the $p\times 64$ standard was developed for video coding and decoding at the rate of $p\times 64$ kbits/s, where p is an integer from 1 to 30. Most of the state-of-the-art video codecs treat source and channel coding separately. This is due to Shannon's source-channel separation principle [2], [3].

 

Figure 1:   Effect of error propagation

Bandwidth reduction is achieved by the source coder by removing the redundancy in the source statistics. Error protection against channel errors is take care of by the channel coder through the addition of redundancy in the transmitted data. However, this separation is justifiable only in the limit of an arbitrary encoding/decoding complexity. But, we know that in practice complexity and delay are the main constraints for communication systems. Therefore, the separation of source and channel coding is no longer optimal [5]. This implies that source and channel coding should depend on each other leading to joint source-channel coding [6].

 

Figure 2:   Proposed H.261 based codec

 

Figure 3:   Quantization table

Channel-matched source quantization has been shown to be an effective way to add error-resilience to the transmission of compressed images and video over noisy channels [6]-[11].

The H.261 standard recommends the use of Huffman encoding to achieve an additional gain in the compression ratio. But, it is known that variable length codes are highly susceptible to channel errors. The critical bits need to be protected from channel errors in order to prevent the complete loss of a transmitted video sequence. If, during transmission some bits are flipped, added or dropped, the synchronization of the decoder to the received bit stream could be lost. This leads to error propagation and the loss of the source symbols. The loss of a few blocks of symbols causes displacements in the received image. Error correcting codes can be used to protect the critical bits from channel errors. Examples of the critical bits are the EOB (end of block) markers and the most significant bit of a source symbol. An error in the most significant bit could cause higher degradation than a corrupted least significant bit. The loss of EOB due to errors leads to catastrophic error propagation as shown in Figure 1. Therefore, the high priority bits need to be protected using channel coding or other methods. But the redundancy due to channel coding reduces the effect of the compression efficiency. Therefore, an optimal trade-off between the rate of the source coder and the channel coder is essential. Forward error correction coding (FEC) is usually employed to reduce the effect of channel errors [12]-[17]. But the cost of using FEC is a reduced information transmission rate. Error resilient entropy coding [10], [18], [19] has recently emerged as a new method for error protection. For an excellent overview of the various error protection techniques we refer to [20].

Channel-matched source quantization and adaptive source rate control based on the channel characteristics are effective ways of reducing the effects of channel noise on the received video signal. However, the performance depends on how fast and reliably the channel parameters (such as the bit error probability p_e) can be estimated. In many applications, p_e is computed using pilot symbol aided techniques. This causes large delays which may not be acceptable for real-time applications. Therefore, it is desirable to reliably estimate the channel statistics and also achieve rate control through adaptive quantization on the fly. An error resilient code can be used in addition to the rate controller to minimize synchronization losses due to entropy coding.

In this project, a channel-matched quantizer, and a fast and reliable simultaneous rate control and channel estimation algorithm based on the stochastic learning automaton [21] for a H.261 based video codec over channels that cause random bit errors is proposed. A stochastic learning automaton at the encoder estimates and tracks the channel bit error probability. We consider p_e=10^-1, 10^-2 and 10^-3 only in this work as they are typical of the wireless channels. The learning is based on a one bit decision feedback from the decoder summarizing the peak signal to noise ratio (PSNR) of the received video frames for a particular choice of the source quantizer. The optimal quantizer (and hence the bit rate) for that channel bit error probability is learnt and selected by the learning automaton using a linear reward inaction (LRI) learning scheme. There is no additional overhead of pilot symbols in this approach. Minimal a priori information about the channel is assumed. The system designer has the flexibility to control the convergence rate of the learning algorithm depending on the reliability and delay constraints. To our knowledge, this is the first attempt in using stochastic learning automaton for rate control in low-bit rate video transmission. In order to prevent sync losses at the decoder, the fast error resilient entropy code in [18] is also used. In Section II the channel-matched quantization technique is discussed. The variable structure stochastic learning automaton is introduced in Section II-C followed by the rate control and channel estimation algorithm. Performance of the proposed algorithm is studied in Section III and concluding remarks are given in Section IV.