Ehsan Maani

Unequal Error Protection for Robust Streaming of Scalable Video Over Packet Lossy Networks

Efficient bit stream adaptation and resilience to packet losses are two critical requirements in scalable video coding for transmission over packet-lossy networks. Various scalable layers have highly distinct importance, measured by their contribution to the overall video quality. This distinction is especially more significant in the scalable H.264/advanced video coding (AVC) video, due to the employed prediction hierarchy and the drift propagation when quality refinements are missing. Therefore, efficient bit stream adaptation and unequal protection of these layers are of special interest in the scalable H.264/AVC video. This paper proposes an algorithm to accurately estimate the overall distortion of decoder reconstructed frames due to enhancement layer truncation, drift/error propagation, and error concealment in the scalable H.264/AVC video. The method recursively computes the total decoder expected distortion at the picture-level for each layer in the prediction hierarchy. This ensures low computational cost since it bypasses highly complex pixel-level motion compensation operations. Simulation results show an accurate distortion estimation at various channel loss rates. The estimate is further integrated into a cross-layer optimization framework for optimized bit extraction and content-aware channel rate allocation. Experimental results demonstrate that precise distortion estimation enables our proposed transmission system to achieve a significantly higher average video peak signal-to-noise ratio compared to a conventional content independent system.

Optimized Bit Extraction Using Distortion Modeling in the Scalable Extension of H.264/AVC

The newly adopted scalable extension of H.264/AVC video coding standard (SVC) demonstrates significant improvements in coding efficiency in addition to an increased degree of supported scalability relative to the scalable profiles of prior video coding standards. Due to the complicated hierarchical prediction structure of the SVC and the concept of key pictures, content-aware rate adaptation of SVC bit streams to intermediate bit rates is a nontrivial task. The concept of quality layers has been introduced in the design of the SVC to allow for fast content-aware prioritized rate adaptation. However, existing quality layer assignment methods are suboptimal and do not consider all network abstraction layer (NAL) units from different layers for the optimization. In this paper, we first propose a technique to accurately and efficiently estimate the quality degradation resulting from discarding an arbitrary number of NAL units from multiple layers of a bitstream by properly taking drift into account. Then, we utilize this distortion estimation technique to assign quality layers to NAL units for a more efficient extraction. Experimental results show that a significant gain can be achieved by the proposed scheme.

Resource Allocation for Downlink Multiuser Video Transmission Over Wireless Lossy Networks

Demand for multimedia services, such as video streaming over wireless networks, has grown dramatically in recent years. The downlink transmission of multiple video sequences to multiple users over a shared resource-limited wireless channel, however, is a daunting task. Among the many challenges in this area are the time-varying channel conditions, limited available resources, such as bandwidth and power, and the different transmission requirements of different video content. This work takes into account the time-varying nature of the wireless channels, as well as the importance of individual video packets, to develop a cross-layer resource allocation and packet scheduling scheme for multiuser video streaming over lossy wireless packet access networks. Assuming that accurate channel feedback is not available at the scheduler, random channel losses combined with complex error concealment at the receiver make it impossible for the scheduler to determine the actual distortion of the sequence at the receiver. Therefore, the objective of the optimization is to minimize the expected distortion of the received sequence, where the expectation is calculated at the scheduler with respect to the packet loss probability in the channel. The expected distortion is used to order the packets in the transmission queue of each user, and then gradients of the expected distortion are used to efficiently allocate resources across users. Simulations show that the proposed scheme performs significantly better than a conventional content-independent scheme for video transmission.

Optimized Bit Extraction Using Distortion Estimation in the Scalable Extension of H.264/AVC

The newly adopted scalable extension of H.264/AVC video coding standard (SVC), demonstrates significant improvements in coding efficiency in addition to an increased degree of supported scalability relative to the scalable profiles of prior video coding standards. For efficient adaptation of SVC bit streams to intermediate bit rates, the concept of quality layers has been introduced in the design of the SVC. The concept of quality layers allow a rate distortion (RD) optimal bit extraction; However, existing Quality Layer assignment methods do not consider all network abstraction layer (NAL) units from different layers for the optimization. In this paper, we first propose a technique to accurately and efficiently estimate the quality degradation resulting from discarding an arbitrary number of NAL units from multiple layers of a bitstream. Then, we utilize this distortion estimation technique to assign quality layers to NAL units for a more efficient extraction. Experimental results show that a significant gain can be achieved by the proposed schemes.

A game theoretic approach to video streaming over peer-to-peer networks

We address the problem of content-aware, foresighted resource reciprocation for media streaming over peer-to-peer (P2P) networks. The envisioned P2P network consists of autonomous and self-interested peers trying to maximize their individual utilities. The resource reciprocation among such peers is modeled as a stochastic game and peers determine the optimal strategies for resource reciprocation using a Markov Decision Process (MDP) framework. Unlike existing solutions, this framework takes the content and the characteristics of the video signal into account by introducing an artificial currency in order to maximize the video quality in the entire network.

A resolution adaptive video compression system

Modern video encoding systems employ block-based,multi-mode, spatio-temporal prediction methods in order to achieve high compression efficiency. A common practice is to transform, quantize and encode the difference between the prediction and the original along with the system parameters. Obviously, it’s crucial to design better prediction and residual encoding methods to obtain higher compression gains. In this work, we examine two such systems which utilize subsampled representations of the sequence and residual data. In the first system, we consider a method for reorganizing, downsampling and interpolating the residual data. In the second system, we propose a new method that employs lower resolution intensity values for spatial and motion-compensated prediction. Both of these methods are macroblock adaptive in the rate-distortion sense. Our experiments show that implementing these methods brings additional compression efficiency compared to the state-of-the-art video encoding standard H.264/AVC.

Packet Scheduling for Scalable Video Streaming Over Lossy Packet Access Networks

Video streaming applications have gained in popularity in recent years. The quality of service offered by such applications is limited by the available transmission rates as well as time-varying conditions, such as, channel fading and network congestion, which lead to packet losses. Scalable video coding techniques that allow for the flexible adaptation of temporal resolution as well as quality of an encoded bitstream can be immensely useful in developing video streaming applications that can adapt to time-varying network and channel conditions. Scalable coding techniques, however, are generally designed to offer progressive refinement, which introduces dependencies between encoded video packets. Therefore, when determining a packet scheduling technique for scalable coded video, the possibility of random packet losses, which might affect the decodability of subsequent packets, must be taken into account. In this paper, we take into account the available transmission rate, possibly time-varying channel conditions, and the possibility of random packet losses, to design a scheduling technique for video packets in a scalable bit-stream. Since the optimal solution to the scheduling problem requires an exhaustive, and therefore, intractable computation, we propose a greedy algorithm that will schedule the optimal packet for transmission at a given transmission opportunity based on the encoded content and the available channel state information. Simulation results show significant gains in performance when the proposed technique is compared to content and channel independent packet scheduling techniques.

Joint source-channel coding and power allocation for video transmission over wireless fading channels

Techniques for modeling and simulating channel conditions play an essential role in efficient real-time video transmission over wireless networks. In this paper, we consider a Finite-State Markov Chain (FSMC) as the channel model to perform Joint Source Channel Coding with power allocation (JSCCPA). The optimization is done in an integrated manner and in one step. Computer simulations are performed to show the advantages of the proposed model.

Two-dimensional channel coding for scalable H.264/AVC video

Efficient bit stream adaptation and resilience to packet losses are two critical requirements in scalable video coding for transmission over packet-lossy networks. These requirements have a greater significance in scalable H.264/AVC video bit streams since missing refinement information in a layer propagates to all lower layers in the prediction hierarchy and causes substantial degradation in video quality. This work proposes an algorithm to accurately estimate the overall distortion of the reconstructed frames due to enhancement layer truncation, drift/error propagation, and error concealment in the scalable H.264/AVC video. This ensures low computational cost since it bypasses highly complex pixel-level motion compensation operations. Simulation results show an accurate distortion estimation at various channel loss rates. The estimate is further integrated into a cross-layer optimization framework for optimized bit extraction and content-aware channel rate allocation. Experimental results demonstrate that precise distortion estimation enables our proposed transmission system to achieve a significantly higher average video PSNR compared to a conventional content independent system.

Content-aware packet scheduling for multiuser scalable video delivery over wireless networks

Wireless video transmission is prone to potentially low data rates and unpredictable degradations due to timevarying channel conditions. Such degradations are difficult to overcome using conventional video coding techniques. Scalable video coding offers a flexible bitstream that can be dynamically adapted to fit the prevailing channel conditions. Advances in scalable video compression techniques, such as the newly adopted scalable extension of H.264/AVC, as well as recent advances in wireless access technologies offer possibilities for tackling this challenge. In this paper, a content-aware scheduling and resource allocation scheme is proposed, that uses a gradient-based scheduling framework in conjunction with scalable video coding techniques to provide multiple high quality video streams over a range of operating conditions to multiple users. Simulation results show that the proposed scheme performs better than conventional content-independent scheduling techniques.