A Framework for Video Network Coding with Multi-generation Mixing

Connectivity, losses, and buffering are factors that directly affect the performance of network coding. These factors affect the ability of intermediate nodes to generate useful encodings; these are encodings that contribute in propagating and recovering data at receiver node(s). It has been shown that in particular scenarios network coding performance can be improved by simply increasing the generation size (k); however, this leads to increasing transmission overhead, encoding complexity and (more importantly) buffer sizes at intermediate nodes. In this paper, we propose a new network coding approach where we employ Multi-Generation Mixing (MGM). MGM eliminates the need to increasing buffer sizes while improving the performance of network coding. Under MGM, we define a mixing set of size m generations that can be network coded (mixed) together. Within each MGM mixing set, a new set of generation packets are mixed with previously transmitted (network coded) generations. This generalized approach provides a great deal of resilience against losses when compared with traditional generation-based network coding. Our analysis of the performance of MGM-based network coding demonstrates significant reduction in required overhead for a given recovery performance at the receivers. We also illustrate the performance of MGM using extensive simulations that provide a useful insight into the viability of MGM-based network coding.

The streaming of real-time audio/video data is very challenging because of the time-varying and unreliable wireless channels, video content characteristics, limited bandwidth, dynamic topology, heterogeneous and distributed environment and high packet loss rate because of wireless interference and channel fading. Because of such open issues present in wireless networks, it is very difficult to satisfy the requirements of audio/video streaming applications such as low delay, low packet loss, jitter control etc. In such scenarios of opportunistic networks where real time data is being streamed from server node to client node, Network Coding and its variants can be used by such nodes to meet different requirements. Network coding changes the role of such multimedia nodes from store and forward to encode data packets. Encoding process includes mathematical operations on data packets. This survey paper has considered number of speculative and practical approaches and cases where network coding or its variant applied either fully or partially on multimedia traffic with the aim to improve performance and to provide protection against packet losses. This review has mainly focused on the opportunity of performance enhancement of MPEG-4 traffic over Wireless Network using Random Linear Network Coding (RLNC) with Multi Generation Mixing (MGM). Using Multi Generation Mixing, packets of greater importance has got more protection, less loss, more reconstruction and recovery of real time data.

Transmission issue of wireless video, as the hugest percentage among mobile traffics, is a challenge problem in wireless/mobile networks. To tackle video transmission problem occurred in heterogeneous networks as well as different devices, scalable video coding (SVC) has been developed. Also, network coding (NC) enables to transmit efficiently for same given bandwidth. In this paper, we propose a simple random linear network coding (RLNC) for SVC transmission with decode and forward (DF) system over wireless relay network. In order to transmit base layer of SVC with high reliability using characteristics of RLNC in DF relay system, we propose a method which re-groups differently basic coding units of base layer and enhancement layers in SVC. The simulation shows our proposed method gains better performance compared to previous methods.

Current digital video applications require delivery of digital video content to different clients over heterogeneous networks. These clients may have different system resources and bandwidth capabilities. Thus, delivering of digital video data compressed at specific spatial resolution, quality levels, frame rates and encoding those video data at specific bit rates according to the client resources and the available network is essential. The characteristics of current video transmission system pose many problems for video delivery and scalable video coding is a highly attractive solution. This thesis investigates the existing video coding standards, especially the newest H.264/advanced video coding and its scalable extension: scalable video coding. In this thesis, we address two issues related to the state-of-the-art scalable video coding: 1) computational complexity reduction; and 2) video coding efficiency enhancement. In the first part of this thesis, we address the complexity issue of spatial, signal-to-noise ratio (SNR), temporal and combined scalable video coding. Our goal is to find a good tradeoff between computational complexity and video coding efficiency. We first establish the need to reduce computational complexity for current H.264/advanced video coding and scalable video coding framework. We then evaluate the mode distribution correlations between the base layer and its enhancement layers for different video scalabilities. After the exhaustive search over all possible block partitions is performed at the base layer, the number of candidate modes for luma and chroma blocks in a macroblock that take part in rate distortion optimization calculation at enhancement layers could be greatly reduced based on the correlations. Finally, adaptive fast mode decision schemes for spatial, SNR and temporal scalable video coding are presented. Our schemes could achieve consistent and significant computational complexity reduction with negligible loss in objective and subjective video quality and insignificant increments in bit rate consumption. The second part of this thesis is to provide a solution for the problem of high computational complexity of scalable video decoders. An adaptive encoding algorithm is proposed to reduce decoder complexity in coarse grain SNR scalable video coding.

Video communication requires sufficient resources to obtain the desired image quality. Availability of bandwidth and screen resolution from different devices on the client end, as well as compression techniques used for transmission also significantly influence to the video quality. Even though the H.264/AVC video compression standard and its scalable extension is known as Scalable Video Coding (SVC) was introduced and successfully addressed the challenge, some weaknesses persist especially for high-resolution video transmissions. The size of the bitstream generated by the SVC standard for ultra-high resolution video compression can only be met if transmitted on a network with a large bandwidth. The Scalable High-Efficiency Video Coding (SHVC) were introduced to overcome problems in the SVC standards. The new video compression standard is proven to reduce bandwidth requirements by up to 46.41 % compared to the SVC standards for the same video quality. This study introduces the performance analysis of SHVC standard video transmission as compared to SVC standard. Both compression standards are transmitted and emulated through network emulator called Mininet-Wifi to get the real transmission picture. The client-server video transmission scheme with different bandwidth and various client position are set in the experiment scenario. It is found that transmission time, packet loss, and video reconstruction quality on scalable video transmission with SHVC standard is better than scalable video transmissions with SVC standard.

Scalable video applications have been gaining popularity during last years. Providing high quality video over wireless networks towards heterogeneous receivers is a challenging task due to erratic and time-varying nature of a wireless channel. Compressed video bit-streams, such as Scalable Video Coding (SVC), are very vulnerable to channel disturbances when transmitted over error-prone wireless channels. Transmission errors not only corrupt the current frame, but also propagate to next frames along motions prediction path. In this paper we propose a novel scheme for transmission of SVC-based streams over Wireless Mesh Network (WMN) with Unequal Error Protection (UEP) based on Appropriate Path Selection (APS) and Network Coding (NC). The scheme tries to select appropriate paths based on Grey Relational Analysis (GRA) for different SVC layers. It chooses the best path for most important layer, base layer, and less stable paths for enhancement layers. Some nearby nodes along the transmission path are selected for recoding their received packets and store them in buffers for some period of time. The nearby nodes coded packets can be transmit to receivers for recovering the lost packets.

We present the design of a peer-to-peer (P2P) live streaming system that uses scalable video coding as well as network coding. The proposed design enables flexible customization of video streams to support heterogeneous receivers, highly utilizes upload bandwidth of peers, and quickly adapts to network and peer dynamics. Our design is simple and modular. Therefore, other P2P streaming systems could also benefit from various components of our design to improve their performance. We conduct an extensive quantitative analysis to demonstrate the expected performance gain from the proposed design. Our analysis uses actual scalable video traces and realistic P2P streaming environments with high churn rates, heterogeneous peers, and flash crowd scenarios. Our results show that the proposed system can achieve: (i) significant improvement in the visual quality perceived by peers (several dBs are observed), (ii) smoother and more sustained streaming rates, (iii) higher streaming capacity by serving more requests from peers, and (iv) more robustness against high churn rates and flash crowd arrivals of peers. This paper shows that the integration of network coding and scalable video coding in P2P live streaming systems yields better performance than current systems that use single-layer streams and proposed systems that use either network coding alone or scalable video coding alone.

Scalable video coding (SVC) employs inter-frame prediction at the base and/or the enhancement layers. Since the base layer can be encoded/decoded independent of the enhancement layers, we consider here the potential gains when prediction at the enhancement layers is delayed to accumulate and incorporate additional future information from the base layer. We build on two basic estimationtheoretic (ET) approaches developed by our group: an ET approach for enhancement layer prediction that optimally combines current base layer with prior enhancement layer information, and our recent ET approach for delayed decoding. The proposed technique fully exploits all the available information from the base layer, including any future frame information, and past enhancement layer information. It achieves considerable gains over zero-delay techniques including both standard SVC, and SVC with optimal ET prediction (but with zero encoding delay).

Scalable High efficiency Video Coding (SHVC) is the scalable extension of the latest video coding standard High Efficiency Video Coding (HEVC). One of the key novelties introduced by SHVC is that it enables hybrid codec scalability. This basically means that the video layers can be encoded with different video standards providing backward compatibility between codecs. In this paper, we propose a software parallel SHVC decoder in hybrid codec scalability configuration. The proposed design consists of an Advanced Video Coding (AVC) decoder for the Base Layer (BL) and a HEVC decoder for the Enhanced Layer (EL). In order to perform Inter Layer Prediction (ILP), a communication of decoding states and outputs is established between the two decoders. While the native frame based parallelism is still allowed within the two decoders, the proposed design also enables the use of frame based parallelism between the two decoders. The proposed software design enables a real time decoding of the HEVC EL at 2160p60 while the AVC base layer is decoded at 1080p60 for ×2 spatial scalability.

HTTP adaptive streaming (HAS) is a state-of-the-art technology for video streaming. Optimal adaptive streaming schemes should be designed to maximize the quality of experience (QoE) in network environments with unstable bandwidths such as wireless networks. QoE is degraded for Advanced Video Coding (AVC)-based HAS due to frequent quality switching and rebuffering in wireless networks. Meanwhile, Scalable Video Coding (SVC)-based HAS can maximize the QoE by flexibly requesting the base layer (BL) and enhancement layer (EL) based on variable network conditions. However, in SVC-based HAS, the required bandwidth is significantly high due to the encoding overhead. In this paper, we propose a layer-assisted video quality adaptation for improving QoE in wireless networks. The proposed scheme employs a video encoding method, composed of multiple BLs and ELs, using both AVC and SVC. The quality of the BL is determined using the buffer occupancy level and measured bandwidth to minimize rebuffering. The quality of the EL is determined using the buffer region and segment quality differences to minimize instability. enhancement dummy (ED) consists of the selected ELs. The layer scheduler decides the segment that is to be requested based on the QoE model according to the BL and ED to maximize the QoE. Experimental results show that the proposed scheme achieves high QoE compared to the existing schemes because the instability is low and no rebuffering occurs.

This paper investigates multicast beamforming for scalable videos in cache-enabled heterogeneous networks, where a macro base station (MBS) and multiple small base stations (SBSs) serve multicast group users on-demand video services. Inspired by the main idea of the scalable video coding, the extension technology of the H.264#x002F;advanced video coding, each video is divided into a base layer (BL) and an enhancement layer (EL). The BL can provide the fundamental viewing quality and adding EL to the received BL can guarantee superior perceptual experience. The MBS and multiple SBSs cache the BLs and ELs of the most popular videos, respectively. Employing the transmission schemes in coordinated multi-point, i.e., parallel transmission and joint transmission, the MBS and SBSs parallelly transmit BLs and ELs while the SBSs cooperatively transmit the ELs if higher video qualities are required. To improve the quality of the received videos for users whose requirements can be satisfied locally, our aim is to maximize the weighted sum rate of them and this problem can be converted into an iterative second-order cone programming problem by successive convex optimization with low complexity. Numerical results demonstrate the advantage of our proposed scheme compared to the benchmark scheme, i.e., only MBS serving the end users, even when the transmit power of each SBS is kept low.

This paper proposes a scalable and credible watermarking algorithm towards Scalable Video Coding (SVC) which aims to build Copyright Protection System (CPS). Firstly, we investigate where to embed watermark to ensure it can be detected in base layer as well as enhancement layer and what does wavelet Just Noticeable Distortion (JND) for video consist of, then a model that combines frequency masking, contrast masking, luminance adaption and temporal masking has been proposed. Finally whether watermark exists or not can be judged by adaptive detection which guarantees the proposed method has a good legal credibility, since its False Alarm Rate (FAR) closes to zero. The scalability and credibility of the proposed method is examined on JSVM (the H.264/MPEG-4 AVC scalable video extension) platform.

Multidimensional Signal, Image, and Video Processing and Coding

Under the typical scenario of high-speed mobility, channel disturbances at the physical layer may disturb the transmission of video base layers. Due to the close dependency of Scalable Video Coding (SVC) on base layers, such disturbances will result in retransmissions and handover delays. Meanwhile, ineffective enhancement layers continue to occupy resources, ultimately causing system performance collapse and further exacerbating physical-layer disturbances. To address this challenge, we propose an edge computing resource coordination optimization scheme for highly dynamic mobile terminals. The scheme first empowers the SVC layered transmission with the local caching capabilities, enabling rapid retransmission of base layer data by employing a Lyapunov optimization framework for transmission queue scheduling. Secondly, we design a strategy for dynamically releasing the enhancement layer (EL) cache. This can mitigate resource waste caused by invalid enhancement layers. Finally, Lyapunov drift optimization is implemented to ensure base layer transmission stability and accelerate system state convergence. Simulation and experimental results demonstrate that the proposed scheme significantly improves video transmission reliability and user experience in highly dynamic network environments.

Scalable Video Coding (SVC) is an on-going standard and the current working draft (WD) is an extension of H.264/AVC. In the WD, exhaustive search technique is employed to select the best coding mode for each macroblock (MB). This technique achieves highest possible coding efficiency, but it results in higher computational complexity. To overcome this, we propose a novel fast mode decision scheme for coarse grain SNR scalability (CGS) in SVC. In this scheme, the mode distribution relationships between the base layer and enhancement layers are employed to reduce the candidate mode set at enhancement layers. The experimental results show that the proposed scheme provides significant reduction in computational complexity with negligible coding loss.