Error resilience video transcoding for wireless communications

Abstract

Video communication through wireless channels is still a challenging problem due to the limitations in bandwidth and the presence of channel errors. Since many video sources are originally coded at a high rate and without considering the different channel conditions that may be encountered later, a means to repurpose this content for delivery over a dynamic wireless channel is needed. Transcoding is typically used to reduce the rate and change the format of the originally encoded video source to match network conditions and terminal capabilities. Given the existence of channel errors that can easily corrupt the video quality, there is also the need to make the bitstream more resilient to transmission errors. In this article, we provide an overview of the error-resilience tools found in todays video coding standards and describe a variety of techniques that may be used to achieve error-resilence video transcoding. IEEE Wireless CommunicationsThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ABSTRACTVideo communication through wireless channels is still a challenging problem due to the limitations in bandwidth and the presence of channel errors. Since many video sources are originally coded at a high rate and without considering the different channel conditions that may be encountered later, a means to repurpose this content for delivery over a dynamic wireless channel is needed. Transcoding is typically used to reduce the rate and change the format of the originally encoded video source to match network conditions and terminal capabilities. Given the existence of channel errors that can easily corrupt the video quality, there is also the need to make the bitstream more resilient to transmission errors. In this article, we provide an overview of the error-resilience tools found in today's video coding standards and describe a variety of techniques that may be used to achieve error-resilience video transcoding.

Error resiliency transcoding and decoding solutions using distributed video coding techniques

“…A more complex transcoder was introduced in [10] which recalculates Motion Vectors (MVs) and coding modes based on a given error model and estimation of distortion at the decoder side. In [11] Xin et al discussed the use of a number of error resiliency tools in terms of the benefits they provide and their impact on coding efficiency from a [12] proposed FEC based multiple description transcoding with unequal loss protection for embedded bit streams such as 3D-SPHIT.…”

Large-scale MIMO is capable of eliminating power-thirsty channel coding for wireless transmission of HEVC/H.265 video

“…However, increased compression efficiency usually makes the coded video stream more vulnerable to packet losses [3,7]. The traditional approach of compensating this side effect is to employ sufficiently powerful error correction codes (ECCs), such as recursive systematic convolutional (RSC) codes, turbo codes, or low-density parity check (LDPC) codes for improving the bit/packet error rate (BER/ PER) [7,8]. Unfortunately, the redundant bits introduced by ECC not only require additional energy to transmit, which is a serious burden for low-power devices such as mobile terminals and wireless sensors, but also decrease the effective transmission rate of a system.…”

Error Resilient for Multiview Video Transmissions with GOP Analysis

“…1 depicts a multiview video coding prediction structure with GOP size of 8, where I, P, and B represents the encoding of pictures in intra mode, predicted mode and bi-predicted mode respectively. The compressed multiview video data are highly sensitive to noise and information is loss due to the removal of statistical and subjective redundancy in the video by the compression scheme [6]. H.264/AVC employs variable length coding (VLC) to achieve higher compression gain.…”