Scalable Multiple Description Coding Research Articles

Reliable rate-adaptive video transmission over cognitive cellular networks using multiple description scalable coding

The promise of high-bandwidth and low-latency wireless applications has placed unprecedented stress on the limited spectrum for future 5G networks. Thus, dynamic spectrum sharing has emerged as a promising solution to increase the capacity and the coverage of future cellular networks. In this paper, we tackle the issue of video delivery over cognitive cellular networks using a combination of the legacy licensed bands as well as the spectrum holes left unused in TV bands. To actively react to fluctuating bandwidth, the original video undergoes a multiple description scalable coding (MDSC), whereby the video sequence is fragmented into odd and even sub-streams, then the decomposed video is hierarchically encoded using H.264/SVC to produce two independent descriptions that refine each other. As long as the available licensed radio resources are not sufficient to deliver all the scalable video layers, the cognitive base station exploits the available portion of TV spectrum using a hybrid interweave and underlay approach to extend the capacity of the legacy infrastructure. Numerical simulations show that the proposed transmission scheme provides enough capacity, up to $$1.62~\mathrm {Mbps}$$ increase, to handle the rising demands in terms of video quality in a timely manner without disrupting the primary link and without connection release. Moreover, the proposed MDSC framework offers an increment of 20% (24%, respectively) in the PSNR (MS-SSIM, respectively) of the received video.

Adaptively post-encoding multiple description video coding

In video coding and transmission, the network conditions are crucial, i.e., bandwidth, delay and other factors. However, how to effectively take these factors into account is still a challenge, as heterogeneous networks are dynamic, and therefore it is difficult to predict their changes. This paper first reports a post-encoding of Scalable Multiple Description Coding (SMDC), which is towards self-adaptive video delivery under different conditions of the network. The proposed scheme contains three major steps: (1) spatiotemporal wavelet transformation of input video sequence; (2) context-based adaptive binary arithmetic coding; and (3) rate allocation under the conditions of the network and analysis of the principle relationship of the rate–distortion slope ratio with packet-loss probability in network links. The performance of the SMDC is compared with that of scalable video coding and scalable H.264, and SMDC is demonstrated to effectively optimize the video delivery to adapt to the dynamics of heterogeneous networks. Part of this work has been published on Data Compression Conference 2009 as a short abstract version [37].

Symmetric Scalable Multiple Description Scalar Quantization

Real-time data delivery over best-effort error-prone packet networks has invigorated the study of robust coding schemes, such as scalable multiple description coding (SMDC). In this context, the paper introduces a novel generic symmetric scalable multiple description quantizer (SSMDSQ) which generates perfectly balanced source descriptions. Novel embedded index assignments are proposed which are used to realize high, as well as medium-to-low redundancy SSMDSQs. Compared to existing designs, it is shown that the proposed quantizer constructions exhibit superior distortion-rate (D-R) performance. Moreover, this paper describes an innovative extension of the Lloyd-Max algorithm in order to optimize symmetric and asymmetric scalable multiple description quantizers. For a family of Generalized Gaussian (GG) source distributions, the proposed optimization algorithm yields on average a significant D-R performance gain over unoptimized quantizers. Furthermore, anchored in the designed SSMDSQs, an SMDC framework is established to realize packet-based transmission over erasure channels. In this framework, transmission strategies are determined for scenarios wherein the average packet loss rate over the transmission link is (a) unknown and (b) can be estimated at the encoder. For both scenarios, SMDC packetized transmission is simulated for a family of GG distributions. Experimental results confirm that, compared to contemporary schemes, the designed quantizer constructions (with or without optimization) account for a significant average gain in signal-to-noise ratio (SNR) for a wide range of packet loss rates.

Low-complexity unbalanced multiple description coding based on balanced clusters for adaptive peer-to-peer video streaming

Multiple description scalable coding based on T+2D wavelet decomposition structure is highly flexible for peer-to-peer (P2P) video streaming. Finding the optimal truncation point of each wavelet-decomposed code block (CB) within each description is an NP-hard problem (Akyol et al., 2007 [1]). For P2P video streaming, it is necessary to implement an efficient multiple description encoder with three attributes; “adaptive” (due to the time-varying capacity of the P2P network links and nodes), “low-complexity” (because of the low processing power of the receiving peer) with arbitrarily “unbalanced descriptions” (because of the unequal capacities of the different sending peers). To design a multiple description encoder with the above mentioned features, we propose a simple clustering algorithm for partitioning the CBs into a limited number of clusters. This simple and efficient clustering algorithm significantly reduces the size of redundancy-rate assignment matrix, such that one can find the optimal channel-aware cluster-level redundancy-rate assignment matrix using a low-complexity full search approach. This approach improves the decoding quality compared to the co-echelon adaptive frameworks (Akyol et al., 2007 [1]; Tillo et al., 2007 [8]) in which a non-optimal heuristic rate assignment pattern is used. Especially for the unbalanced P2P scenario (which is the usual case), the performance gain of the proposed approach over the one presented in Tillo et al. (2007) [8] (generating only balanced descriptions) is significant (0.95–3.0 dB). In addition, the proposed clustering approach may be analytically represented by closed-form relations for low-complexity computation of the optimal encoding parameters. Our complexity analysis shows that the proposed approach requires 52–96% less computations compared to the framework in Akyol et al. (2007) [1]. Therefore, an efficient real-time post-encoding adaptation mechanism may be realized. The simulation results demonstrate that the adaptive proposed framework outperforms the approach presented in Akyol et al. (2007) [1] by (0.26–0.95 dB) and the non-adaptive multiple description coding by (1.1–2.3 dB).

Multiple description scalable coding algorithm of remote sensing image based on DCT layered structure

Multiple description scalable coding algorithm of remote sensing image based on DCT layered structure

SVC-based scalable multiple description video coding and optimization of encoding configuration

It is well known that multiple multicast trees can avoid single point of failures in peer-to-peer (P2P) streaming originating from a single source by providing path diversity. Multiple description video coding is well suited for serving video over multiple multicast trees, such that each description can be streamed over a different tree. In scalable multiple description coding (SMDC), each description is scalable coded (e.g., derived from a single scalable video coding (SVC)-compliant bitstream) so that descriptions can be efficiently adapted to the available rate of each link for effective congestion control. This paper proposes (i) new SMDC generation methods based on various combinations of video segments (GOPs or fames) coded at high and low rates (one or more layers) and (ii) a multiple-objective optimization (MOO) framework for selection of the best encoding configuration of the proposed SMDC from a set of candidates in order to achieve the best tradeoff between redundancy and reliability (measured by end-to-end rate-distortion performance over a range of extraction points for a set of packet loss probabilities), while maximizing the range of extraction points of each scalable description. The optimization is performed over selected SVC encoding parameters and MD generation alternatives that feature various levels of redundancy at a fixed total rate for all descriptions. The framework is generic to allow optimization over other SVC encoding variables and MD generation methods, or just over the SVC encoding variables without MD, if desired. Results of Monte-Carlo simulation of SMDC streaming with two descriptions over two paths with packet losses are provided to demonstrate performance of the proposed methods.

Scalable multiple description coding with side information using motion interpolation

Problems with modern video delivery systems is that decoder fails to decode video when it is transmitted over error prone channels, due to varying characteristics of a wireless channel or congestion in wired networks. Thus introducing mismatch into the decoded video stream. Scalable video coding (SVC) encounters such a problem as the loss of a lower layer prevents all higher layers being decoded, even if the higher layers are correctly received. Multiple description coding (MDC) can help solve such a problem. We propose a scalable MDC architecture that creates an even and odd streams with residual information regarding the other stream. Using such a method creates redundancies that can improve the decoded reconstruction of a frame however increasing the bit rate of a given stream. To solve this a motion interpolation technique is used to improve the construction of side information thus reducing the bit rate but improving the frame quality. Experimental results show that motion interpolation scheme can improve the frame quality by 6 dB over a pixel based interpolation scheme for a number of video sequences Experimental results also show that this method can give 40% saving in bit rate when compared to a pixel based interpolation. Using such a method makes the system a viable target for mobile communications.

Scalable Multiple-Description Image Coding Based on Embedded Quantization

Scalable multiple-description (MD) coding allows for fine-grain rate adaptation as well as robust coding of the input source. In this paper, we present a new approach for scalable MD coding of images, which couples the multiresolution nature of the wavelet transform with the robustness and scalability features provided by embedded multiple-description scalar quantization (EMDSQ). Two coding systems are proposed that rely on quadtree coding to compress the side descriptions produced by EMDSQ. The proposed systems are capable of dynamically adapting the bitrate to the available bandwidth while providing robustness to data losses. Experiments performed under different simulated network conditions demonstrate the effectiveness of the proposed scalable MD approach for image streaming over error-prone channels.

Multiple description scalable video coding based on 3D lifted wavelet transform

In this work, a new method to deal with the unconnected pixels in motion compensated temporal filtering (MCTF) is presented, which is designed to improve the performance of 3D lifted wavelet coding. Furthermore, multiple description scalable coding (MDSC) is investigated, and novel MDSC schemes based on 3D wavelet coding are proposed, using the lifting imple- mentation of temporal filtering. The proposed MDSC schemes can avoid the mismatch problem in multiple description video coding, and have high scalability and robustness of video transmission. Experimental results showed that the proposed schemes are feasible and adequately effective.

Scalable multiple description coding and distributed video streaming in 3G mobile communications

AbstractThis paper proposes a distributed multimedia delivery mobile network for video streaming in 3rd generation (3G) mobile communications. The joint design of layered coding (LC) and multiple description coding (MDC) is employed to address the bandwidth fluctuations and packet loss problems in the wireless network and to further enhance the error resilience tools in MPEG‐4. A new Internet protocol (IP) differentiated services (DiffServ) video marking algorithm is presented to support an unequal error protection of the LC components. Both intra‐RAN (radio access network) handoff and inter‐RAN handoff procedures are discussed, which provide path diversity to combat streaming video outage due to handoff in the universal mobile telecommunications system (UMTS). Computer simulation results demonstrate that: (1) the newly proposed IP DiffServ video marking algorithm is more suitable for video streaming in an IP mobile network as compared with the previously proposed algorithm, and (2) the proposed handoff procedures have better performance in terms of handoff latency, end‐to‐end delay and handoff scalability than that in UMTS. Copyright © 2005 John Wiley & Sons, Ltd.