Perceptual Quality Metric as a Performance Tool for ATM Adaptation of MPEG-2 Based Multimedia Applications

In this paper we study the perceptual impact of data loss on MPEG-2 video coded streams transmitted over an ATM network. This impact is measured using a perceptual quality metric based on a spatio-temporal model of the human visual system. Video streams have been transmitted on top of both new network and ATM adaptation layers which provide a robust transmission by applying per-cell sequence numbering combined with a selective Forward Error Correction (FEC) mechanism. We compare their performance against a transmission over AAL5. Results show that the proposed AAL behaves better in terms of both network performance and perceived quality of the MPEG-2 decoded sequenc

New Network and ATM Adaptation Layers for Interactive MPEG-2 Video Communications: A Performance Study Based on Psychophysics

In this paper, we present new Network and ATM Adaptation Layers for interactive MPEG-2 video communications. These layers provide reliable transmission by applying per-cell sequence numbering combined with a selective Forward Error Correction (FEC) mechanism based on Burst Erasure codes. We compare the performance of the proposed scheme with a transmission over AAL5 by simulating the transport of an MPEG-2 sequence over an ATM network. Performance is measured in terms of Cell Loss Ratio (CLR) and user perceived quality. The proposed layers achieve significant improvements on the cell loss figures obtained for AAL5 under the same traffic conditions. To evaluate the impact of cell losses at the user level, we apply a perceptual quality metric to the decoded MPEG-2 sequences. According to the computational metric and subjective rating, the proposed multimedia AAL (MAAL) achieves a graceful quality degradation. The application of a selective FEC achieves an even smoother image quality degradation with a low overhea

AMISP: A Complete Content-Based MPEG-2 Error-Resilient Scheme

We address a new error-resilient scheme for broadcast quality MPEG-2 video streams to be transmitted over lossy packet networks. A new scene-complexity adaptive mechanism, namely Adaptive MPEG-2 Information Structuring (AMIS) is introduced. AMIS modulates the number of resynchronization points (i.e., slice headers and intra-coded macroblocks) in order to maximize the perceived video quality, assuming that the encoder is aware of the underlying packetization scheme, the packet loss probability (PLR) and the error concealment technique implemented at the decoding side. The end-to-end video quality depends both on the encoding quality and the degradation due to data loss. Therefore, AMIS constantly determines the best compromise between the rate allocated to encode pure video information and the rate aiming at reducing the sensitivity to packet loss. Experimental results show that AMIS dramatically outperforms existing structuring techniques, thanks to its efficient adaptivity. We then extend AMIS with a Forward Error Correction (FEC) based Protection algorithm to become AMISP. AMISP triggers the insertion of FEC packets in the MPEG-2 video packet stream. Finally, the performances of the AMISP scheme in an MPEG-2 over RTP/UDP/IP scenario are evaluated

Batched Patch Caching for Streaming Media

We elaborate on a scheme that combines batch patching at an origin server and prefix/interval caching at an edge server receiving the clients' requests. We derive a cost function that factors in the aggregate backbone rate, the cache occupancy and the disk bandwidth utilization. We define the optimal batched patch caching strategy as a function of the client request rate. Finally, we show how various strategies including full caching, no caching and pure prefix caching with no patching are optimal derivations of our scheme under different request rates. We demonstrate the benefits of our scheme compared to classical streaming strategies

Joint Source/FEC Rate Selection for Optimal MPEG-2 Video Delivery

This paper deals with the optimal allocation of MPEG-2 encoding and media-independent FEC rates under a total given bandwidth. The optimality is defined in terms of minimum perceptual end-to-end distortion given a set of video and network parameters. We first derive the set of equations leading to the residual loss process parameters. That is, the packet loss ratio and the average burst length after FEC decoding. We then show that the perceptual source distortion decreases exponentially with the MPEG-2 source rate. We also demonstrate that the perceptual distortion due to data lossis directly proportional to the number of lost macroblocks, and therefore decreases with the amount of channel protection. Finally, we derive the global set of equations that lead to the optimal dynamic rate allocation. The optimal distribution is shown to outperform classical FEC schemes, thanks to its adaptivity to the scene complexity, to the available bandwidth and to the network conditions

Distortion-Buffer Optimized TCP Video Streaming

This paper presents a distortion optimized streaming algorithm for on-demand streaming of multimedia. Given the pre-encoded packets of a multimedia stream, we propose an algorithm for selecting an appropriate subset of these packets such that the overall client distortion is minimized. This minimization is performed within the rate constraints imposed by the communication channel. In the interest of computation it is desirable to limit the horizon (i.e. the look-ahead) over which the optimization is performed. Inevitably, shortening the horizon leads to sub-optimal results. We alleviate the impact due to this through the introduction of a buffering constraint that stipulates a minimum desired buffer occupancy at all time during the streaming session. We pose this problem as a Lagrangian minimization the solution to which is obtained through an iterative descent algorithm. We demonstrate the efficacy of the proposed approach through empirical evaluation

Signal Processing Challenges in Distributed Stream Processing Systems

Distributed stream processing represents a novel computing paradigm where data, sensed externally and possibly preprocessed, is pushed asynchronously to various connected computing devices with heterogeneous capabilities for processing. It enables novel applications typically characterized by the need to process high-volume data streams in a timely and responsive fashion. Some example applications include sensor networks, location-tracking services, distributed speech recognition, and network management. Recent work in large-scale distributed stream processing tackle various research challenges in both the application domain as well as in the underlying system. The main focus of this paper is to highlight some of the signal processing challenges such a novel computing framework brings. We first briefly introduce the main concepts behind distributed stream processing. Then we define the notion of relevant information from two related information-theoretic approaches. Finally, we browse existing techniques for sensing and quantizing the information given the set of classification, detection and estimation tasks, which we refer to as task-driven signal processing. We also address some of the related unexplored research challenges