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On the accuracy and complexity of rate-distortion models for fine-grained scalable video sequences
, 2006
"... Rate-distortion (R-D) models are functions that describe the relationship between the bitrate and expected level of distortion in the reconstructed video stream. R-D models enable optimization of the received video quality in different network conditions. Several R-D models have been proposed for, t ..."
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Rate-distortion (R-D) models are functions that describe the relationship between the bitrate and expected level of distortion in the reconstructed video stream. R-D models enable optimization of the received video quality in different network conditions. Several R-D models have been proposed for, the increasingly becoming popular, fine-grained scalable video sequences. However, the models ’ relative performance has not been thoroughly analyzed. Moreover, the time complexity of each model is not known, nor is the range of bitrates in which the model produces valid results. This lack of quantitative performance analysis makes it difficult to select the model that best-suits a target streaming system. In this paper, we classify, analyze, and rigorously evaluate all R-D models proposed for FGS coders in the literature. We classify R-D models into three categories: analytic, empirical, and semi-analytic. We describe the characteristics of each category. We analyze the R-D models by following their mathematical derivations, scrutinizing the assumptions made, and explaining when the assumptions fail and why. In addition, we implement all R-D models, a total of eight, and evaluate them using a diverse set of video sequences. In our evaluation, we consider various source characteristics, diverse channel conditions, different encoding/decoding parameters, different frame types, and several performance metrics including accuracy, range of applicability, and time complexity of each model. We also present clear systematic ways (pseudo codes) for constructing various R-D models from a given video sequence. Based on our experimental results, we present a justified list of recommendations on selecting the best R-D models for video-on-demand, video conferencing, real-time, and peer-to-peer streaming systems.
Layered Internet Video Engineering (LIVE): Network-Assisted Bandwidth Sharing and Transient Loss Protection for Scalable Video Streaming
- IEEE, 2010. Deliverable D5.2 Evaluation of Congestion Control via the Scalable Video Codec Page 55 of 57 Copyright © OCEAN Consortium
, 2011
"... Abstract-This paper presents a novel scheme, Layered Internet Video Engineering (LIVE), in which network nodes feed back virtual congestion levels to video senders to assist both media-aware bandwidth sharing and transient loss protection. The video senders respond to such feedback by adapting the ..."
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Abstract-This paper presents a novel scheme, Layered Internet Video Engineering (LIVE), in which network nodes feed back virtual congestion levels to video senders to assist both media-aware bandwidth sharing and transient loss protection. The video senders respond to such feedback by adapting the rates of encoded H.264/SVC streams based on their respective video rate-distortion (R-D) characteristics. The same feedback is employed to calculate the amount of forward error correction (FEC) protection for combating transient losses. Simulation studies show that LIVE can minimize the total distortion of all participating video streams and hence maximize their overall quality. At steady state, video streams experience no queueing delays or packet losses. In face of transient congestion, the network-assisted adaptive FEC scheme effectively protects video packets from losses while minimizing overhead. Our theoretical analysis further guarantees system stability for an arbitrary number of streams with heterogenous round trip delays below a prescribed limit. Finally, we show that LIVE streams can coexist with TCP flows within the existing explicit congestion notification (ECN) framework. Index Terms-scalable video streaming, media-aware bandwidth sharing, forward error correction (FEC), explicit congestion notification (ECN)
Structuring Multi-Layer Scalable Streams to Maximize Client-Perceived Quality
"... Abstract — Recent video coders, such as H.264/SVC, can encode a video stream into multiple layers, each with a different rate. Moreover, each layer can either be coarse-grained scalable (CGS) or fine-grained scalable (FGS). FGS layers support wider ranges of client bandwidth than CGS layers, but suf ..."
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Abstract — Recent video coders, such as H.264/SVC, can encode a video stream into multiple layers, each with a different rate. Moreover, each layer can either be coarse-grained scalable (CGS) or fine-grained scalable (FGS). FGS layers support wider ranges of client bandwidth than CGS layers, but suffer from higher coding inefficiency. Currently there are no systematic ways in the literature to determine the optimal stream structure that renders the best average quality for all clients. In this paper, we formulate an optimization problem to determine the optimal rate and encoding granularity (CGS or FGS) of each layer in a scalable video stream that maximizes a system-defined utility function for a given client distribution. We design an efficient, yet optimal, algorithm to solve this optimization problem. Our algorithm is general in the sense that it can employ arbitrary utility functions for clients. We implement our algorithm and verify its optimality. We show how various structuring of scalable video streams affect individual client utilities. We compare our algorithm against a heuristic algorithm that has been used before in the literature, and we show that our algorithm outperforms the other one in all cases. I.
Prioritized Flow Optimization with Multi-Path and Network Coding Based Routing for Scalable Multirate Multicasting
"... Abstract—In this paper, we study performance optimization for scalable video coding and multicast over networks. Multi-path video streaming, network coding based routing, and network flow control are jointly optimized to maximize a network utility function defined over heterogeneous receivers. Conte ..."
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Abstract—In this paper, we study performance optimization for scalable video coding and multicast over networks. Multi-path video streaming, network coding based routing, and network flow control are jointly optimized to maximize a network utility function defined over heterogeneous receivers. Content priority of video coding layers is considered during the flow routing to determine the optimal multicast paths and associated data rates for each layer. Our optimization scheme attempts to find content distribution meshes with minimum path costs for each video coding layer while satisfying the inter-layer dependency during scalable video coding. Based on primal decomposition and primal-dual analysis, we develop a decentralized algorithm with two optimization levels to solve the performance optimization problem. We also prove the stability and convergence of the proposed iterative algorithm using Lyapunov theories. Extensive experimental results demonstrate that the proposed algorithm not only achieves the max-flow throughput using network coding, but also provides better video quality with balanced layered access for heterogeneous receivers. Index Terms—Multicast, network coding, resource allocation, scalable video coding. I.
Partitioning of Multiple Fine-Grained Scalable Video Sequences Concurrently Streamed to Heterogeneous Clients
, 2007
"... Abstract—Fine-grained scalable (FGS) coding of video streams has been proposed in the literature to accommodate client heterogeneity. FGS streams are composed of two layers: a base layer, which provides basic quality, and a single enhancement layer that adds incremental quality refinements proportio ..."
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Abstract—Fine-grained scalable (FGS) coding of video streams has been proposed in the literature to accommodate client heterogeneity. FGS streams are composed of two layers: a base layer, which provides basic quality, and a single enhancement layer that adds incremental quality refinements proportional to number of bits received. The base layer uses nonscalable coding which is more efficient in terms of compression ratio than scalable coding used in the enhancement layer. Thus for coding efficiency larger base layers are desired. Larger base layers, however, disqualify more clients from getting the stream. In this paper, we experimentally analyze this coding efficiency gap using diverse video sequences. For FGS sequences, we show that this gap is a non-increasing function of the base layer rate. We then formulate an optimization problem to determine the base layer rate of a single sequence to maximize the average quality for a given client bandwidth distribution. We design an optimal and efficient algorithm (called FGSOPT) to solve this problem. We extend our formulation to the multiple-sequence case, in which a bandwidth-limited server concurrently streams multiple FGS sequences to diverse sets of clients. We prove that this problem is NP-Complete. We design a branch-and-bound algorithm (called MFGSOPT) to compute the optimal solution. MFGSOPT runs fast for many typical cases because it intelligently cuts the search space. In the worst case, however, it has exponential time complexity. We also propose a heuristic algorithm (called MFGS) to solve the multiple-sequence problem. We experimentally show that MFGS produces near-optimal results and it scales to large problems: it terminates in less than 0.5 s for problems with more than 30 sequences. Therefore, MFGS can be used in dynamic systems, where the server periodically adjusts the structure of FGS streams to suit current client distributions. Index Terms—Fine-grained scalable coding, multimedia communication, quality optimization, video streaming. I.
Optimal Coding of Multilayer and Multiversion Video Streams
"... Abstract—Traditional video servers partially cope with heterogeneous client populations by maintaining a few versions of the same stream with different bit rates. More recent video servers leverage multilayer scalable coding techniques to customize the quality for individual clients. In both cases, ..."
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Abstract—Traditional video servers partially cope with heterogeneous client populations by maintaining a few versions of the same stream with different bit rates. More recent video servers leverage multilayer scalable coding techniques to customize the quality for individual clients. In both cases, heuristic, error-prone, techniques are currently used by administrators to determine either the rate of each stream version, or the granularity and rate of each layer in a multilayer scalable stream. In this paper, we propose an algorithm to determine the optimal rate and encoding granularity of each layer in a scalable video stream that maximizes a system-defined utility function for a given client distribution. The proposed algorithm can be used to compute the optimal rates of multiversion streams as well. Our algorithm is general in the sense that it can employ arbitrary utility functions for clients. We implement our algorithm and verify its optimality, and we show how various structuring of scalable video streams affect the client utilities. To demonstrate the generality of our algorithm, we consider three utility functions in our experiments. These utility functions model various aspects of streaming systems, including the effective rate received by clients, the mismatch between client bandwidth and received stream rate, and the client-perceived quality in terms of PSNR. We compare our algorithm against a heuristic algorithm that has been used before in the literature, and we show that our algorithm outperforms it in all cases. Index Terms—Multimedia communication, scalable coding, video quality optimization, video streaming. I.
Optimal Partitioning of Fine-Grained Scalable Video Streams
"... The increased popularity of video streaming over the Internet attracts numerous clients. These clients are quite heterogeneous in terms of network bandwidth and processing capacity. To accommodate this heterogeneity, fine-grained scalable (FGS) coding of video streams has been proposed in the litera ..."
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The increased popularity of video streaming over the Internet attracts numerous clients. These clients are quite heterogeneous in terms of network bandwidth and processing capacity. To accommodate this heterogeneity, fine-grained scalable (FGS) coding of video streams has been proposed in the literature. FGS streams are composed of two layers: base layer, which provides basic quality, and a single enhancement layer that adds incremental quality refinements proportional to the number of bits received. The base layer uses nonscalable coding which is is more efficient in terms of compression ratio than scalable coding used in the enhancement layer. Thus for coding efficiency larger base layers are desired. Larger base layers, however, disqualify more clients from getting the stream. In this paper, we study and quantify the trade-off between the coding efficiency and the range of clients that can be supported. Then, we design an efficient algorithm to compute the optimal size of the base layer that will yield the best video quality for a given client distribution. We implement our algorithm and apply it on video sequences with different characteristics. Our experimental results show that our algorithm improves the average perceived quality for all clients. 1.
Distributed media rate . . .
- SIGNAL PROCESSING: IMAGE COMMUNICATION
, 2008
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DOI 10.1007/s00530-014-0421-x Multimedia Systems REGULAR PAPER ALD: adaptive layer distribution for scalable video
, 2014
"... high-quality viewing, with lower transmission cost, relative to MDC, irrespective of clip type. This highlights the bene-fits of selective packetisation in addition to intuitive encod-ing and transmission. ..."
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high-quality viewing, with lower transmission cost, relative to MDC, irrespective of clip type. This highlights the bene-fits of selective packetisation in addition to intuitive encod-ing and transmission.
