An unequal packet loss resilience scheme for video over the Internet

We present an unequal packet loss resilience scheme for robust transmission of video over the Internet. By jointly exploiting the unequal importance existing in different levels of syntax hierarchy in video coding schemes, GOP-level and Resynchronization-packet-level Integrated Protection (GRIP) is designed for joint unequal loss protection (ULP) in these two levels using forward error correction (FEC) across packets. Two algorithms are developed to achieve efficient FEC assignment for the proposed GRIP framework: a model-based FEC assignment algorithm and a heuristic FEC assignment algorithm. The model-based FEC assignment algorithm is to achieve optimal allocation of FEC codes based on a simple but effective performance metric, namely distortion-weighted expected length of error propagation, which is adopted to quantify the temporal propagation effect of packet loss on video quality degradation. The heuristic FEC assignment algorithm aims at providing a much simpler yet effective FEC assignment with little computational complexity. The proposed GRIP together with any of the two developed FEC assignment algorithms demonstrates strong robustness against burst packet losses with adaptation to different channel status.     

Rate-distortion optimized streaming of packetized media

This paper addresses the problem of streaming packetized media over a lossy packet network in a rate-distortion optimized way. We show that although the data units in a media presentation generally depend on each other according to a directed acyclic graph, the problem of rate-distortion optimized streaming of an entire presentation can be reduced to the problem of error-cost optimized transmission of an isolated data unit. We show how to solve the latter problem in a variety of scenarios, including the important common scenario of sender-driven streaming with feedback over a best-effort network, which we couch in the framework of Markov decision processes. We derive a fast practical algorithm for nearly optimal streaming in this scenario, and we derive a general purpose iterative descent algorithm for locally optimal streaming in arbitrary scenarios. Experimental results show that systems based on our algorithms have steady-state gains of 2-6 dB or more over systems that are not rate-distortion optimized. Furthermore, our systems essentially achieve the best possible performance: the operational distortion-rate function of the source at the capacity of the packet erasure channel.     

On packetization of embedded multimedia bitstreams

We study the problem of packetizing embedded multimedia bitstreams to improve the error resilience of source (compression) codes. This problem is important because of the increasing popularity of embedded compression methodology and its suitability for scalable streaming media over IP or/and mobile IP. We study various packetization schemes against packet erasure at both low and high bit rates. Maximizing packetization efficiency for embedded bitstreams is formulated as a discrete optimization problem and globally optimal packetization (OP) algorithms are proposed under different settings. Suboptimal packetization algorithms are also devised to reduce the complexity of the OP algorithms. In order to assess their effectiveness, the proposed packetization algorithms are used to packetize embedded image and video bitstreams with simulated packet loss. Experimental results show that our OP algorithms slightly outperforms suboptimal ones. In addition to confirming the superiority of the OP algorithms, these results also provide justification of heuristic packetization methods published in the literature     

Effective bandwidth based scheduling for streaming media

We propose a class of rate-distortion optimized packet scheduling algorithms for streaming media by generating a number of nested substreams, with more important streams embedding less important ones in a progressive manner. Our goal is to determine the optimum substream to send at any moment in time, using feedback information from the receiver and statistical characteristics of the video. To do so, we model the streaming system as a queueing system, compute the run-time decoding failure probability of a group of picture in each substream based on effective bandwidth approach, and determine the optimum substream to be sent at that moment in time. We evaluate our scheduling scheme with various video traffic models featuring short-range dependency (SRD), long-range dependency (LRD), and/or multifractal properties. From experiments with real video data, we show that our proposed scheduling scheme outperforms the conventional sequential sending scheme.     

Approximations for Strategies and Termination

The theorem of Huet and Lévy stating that for orthogonal rewrite systems (i) every reducible term contains a needed redex and (ii) repeated contraction of needed redexes results in a normal form if the term under consideration has a normal form, forms the basis of all results on optimal normalizing strategies for orthogonal rewrite systems. However, needed redexes are not computable in general.

In the paper we illustrate, based on the framework introduced in [6], how the use of approximations and their associated tree automata results allows one to obtain decidable conditions in a simple and elegant way.

We further show how the very same ideas can be used to improve [18] the dependency pair method of Arts and Giesl [1] for proving termination of rewrite systems automatically. More precisely, we show how approximations and tree automata techniques provide a better estimation of the dependency graph. This graph determines the ordering constraints that have to be solved in order to conclude termination. Furthermore, we present a new estimation of the dependency graph that does not rely on computationally expensive tree automata techniques.     

Branch and bound algorithms for rate-distortion optimized media streaming

We consider the problem of rate-distortion optimized streaming of packetized multimedia data over a single quality-of-service network using feedback and retransmissions. For a single data unit, we prove that the problem is NP-hard and provide efficient branch and bound algorithms that are much faster than the previously best solution based on dynamic programming. For a group of interdependent data units, we show how to compute optimal solutions with branch and bound algorithms. The branch and bound algorithms for a group of data units are much slower than the current state of the art, a heuristic technique known as sensitivity adaptation. However, in many real-world situations, they provide a significantly better rate-distortion performance.     

视频流传输中的网络编码综述

网络视频应用增长迅猛,利用网络编码（NC）来提高网络吞吐量和传输可靠性进而改善视频流传输质量成为研究热点。针对如何优化网络编码进行视频流传输这一问题,必须要结合视频流自身特性作出改进并综合考虑所处的网络环境,才能充分发挥网络编码的优势。首先回顾了网络编码的基本概念和方法;然后对网络编码应用于视频流传输时需要考虑的视频业务特性,包括进行不等差错保护以优先保障重要等级视频数据包、降低数据包传输延迟以满足视频流实时性需求、增强网络差错恢复策略以提升传输可靠性等作了分析和归纳;接着阐述了基于网络编码的视频流传输策略在包括P2P、多源协作及内容中心网络等典型场景中的应用;最后对网络编码应用于视频流传输的研究趋势进行展望。     

An integrated source transcoding and congestion control paradigmfor video streaming in the Internet

In this work we present an end-to-end optimized video streaming system comprising of synergistic interaction between a source packetization strategy and an efficient and responsive, TCP-friendly congestion control protocol [Linear Increase Multiplicative Decrease with History (LIMD/H)]. The proposed source packetization scheme transforms a scalable/layered video bitstream so as to provide graceful resilience to network packet drops. The congestion control mechanism provides low variation in transmission rate in steady state and at the same time is reactive and provably TCP-friendly. While the two constituent algorithms identified above are novel in their own right, a key aspect of this work is the integration of these algorithms in a simple yet effective framework. This “application-transport” layer interaction approach is used to maximize the expected delivered video quality at the receiver. The integrated framework allows our system to gracefully tolerate and quickly react to sudden changes in the available connection capacity due to the onset of congestion, as verified in our simulations     

Efficient algorithms of video replication and placement on a cluster of streaming servers

A cost-effective approach to building up scalable video streaming servers is to couple a number of streaming servers together in a cluster so as to alleviate the inherent storage and networking constraints of streaming services. In this article, we investigate a crucial problem of video replication and placement on a distributed storage cluster of streaming servers for high quality and high availability services. We formulate it as a combinatorial optimization problem with objectives of maximizing the encoding bit rate and the number of replicas of each video and balancing the workload of the servers. The objectives are subject to the constraints of the storage capacity and the outgoing network-I/O bandwidth of the servers. Under the assumption of single fixed encoding bit rate for all video objects with different popularity values, we give an optimal replication algorithm and a bounded placement algorithm, respectively. We further present an efficient replication algorithm that utilizes the Zipf-like video popularity distributions to approximate the optimal solutions, which can reduce the complexity of the optimal replication algorithm. For video objects with scalable encoding bit rates, we propose a heuristic algorithm based on simulated annealing. We conduct a comprehensive performance evaluation of the algorithms and demonstrate their effectiveness via simulations over a synthetic workload set.     

Rollout strategies for sequential fault diagnosis

Test sequencing is a binary identification problem wherein one needs to develop a minimal expected cost testing procedure to determine which one of a finite number of possible failure sources, if any, is present. The problem can be solved optimally using dynamic programming or AND/OR graph search methods (AO/sup */, CF, and HS). However, for large systems, the associated computation with dynamic programming or AND/OR graph search methods is substantial, due to the rapidly increasing number of OR nodes (denoting ambiguity states) and AND nodes (denoting tests) in the search graph. In order to overcome the computational explosion, the one-step or multistep lookahead heuristic algorithms have been developed to solve the test sequencing problem. In this paper, we propose to apply rollout strategies, which can be combined with the one-step or multistep lookahead heuristic algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the one-step or multistep lookahead heuristic algorithms. The rollout strategies are illustrated and tested using a range of real-world systems. We show computational results, which suggest that the information-heuristic based rollout policies are significantly better than other rollout policies based on Huffman coding and entropy.     

HEURISTIC ALGORITHMS FOR SURVIVABLE P2P MULTICASTING

The growing volume of Internet traffic, increasing popularity of streaming services, and limited scalability of existing network techniques trigger the need to develop new delivery solutions based on a multicasting approach. Multicasting—defined as a one-to-many delivery technique—enables effective distribution of many kinds of content to end users. In this article we focus on peer-to-peer (P2P) multicasting, which combines concepts of P2P systems and multicasting solutions; in other words, the multicast tree is constructed using end hosts (peers). Because P2P multicasting can be applied to deliver content with high reliability requirements, we introduce to P2P multicasting additional survivability constraints that guarantee delivery of content in the case of network failures. We formulate a mixed-integer programming (MIP) optimization problem of survivable P2P multicasting. Because the problem is nondeterministic polynomial time (NP)-hard and exact methods such as branch-and-cut can be applied for only a relatively small problem instance, we propose two heuristic algorithms based on evolutionary approach and Tabu Search methods. Extensive computational experiments show that both heuristic algorithms provide results close to optimal—the average gap to optimal results is 0.26% and 5.15% in the case of evolutionary and Tabu Search methods, respectively.     

一类MPEG-4 FGS流视频传输的质量定义及算法

对MPEG-4 FGS流视频的客观质量及质量变化指标的严格定义,是保证其在IP上有效传输以满足终端表示的可达主观质量的必要手段.基于PSNR（MSE）建立了MPEG-4 FGS流视频的相关客观质量和质量变化指标定义,提出了视频分段场景基础上的质量最优化问题,通过状态迁移图和动态规划解决了FGS流视频的最优传输策略问题.并且用试验数据对FGS流视频的优化传输的控制粒度及实时传输算法进行了对比分析.     

A graph theoretical approach to determine a join reducer sequencein distributed query processing

Semijoin has traditionally been relied upon to reduce the cost of data transmission for distributed query processing. However, judiciously applying join operations as reducers can lead to further reduction in the amount of data transmission required. In view of this fact, we explore the approach of using join operations as reducers in distributed query processing. We first show that the problem of determining a sequence of join operations for a query can be transformed to that of finding a specific type of set of cuts to the corresponding query graph, where a cut to a graph is a partition of nodes in that graph. Then, in light of this concept, we prove that the problem of determining the optimal sequence of join operations for a given query graph is of exponential complexity, thus justifying the necessity of applying heuristic approaches to solve this problem. By mapping the problem of determining a sequence of join reducers into the one of finding a set of cuts, we develop (for tree and general query graphs, respectively) efficient heuristic algorithms to determine a join reducer sequence for distributed query processing. The algorithms developed are based on the concept of divide and conquer and are of polynomial time complexity. Simulation is performed to evaluate these algorithms     

Classification-Based System For Cross-Layer Optimized Wireless Video Transmission

Joint optimization strategies across various layers of the protocol stack have recently been proposed for improving the performance of real-time video transmission over wireless networks. In this paper, we propose a new, low complexity system for determining the optimal cross-layer strategies for wireless multimedia transmission based on classification and machine learning techniques. We first determine offline the optimal cross-layer strategy for various video sequences and channel conditions (training data). Subsequently, we extract relevant and easy to compute content features, encoder-specific parameters, and channel resources from the training data, and train a statistical classifier based on these optimal results. At run-time, we predict using the classifier the optimal cross-layer compression and transmission strategy using these simple, on-the-fly computed features. Hence, we consider the complex problem of finding the optimal cross-layer strategy during the training phase only, and rely at transmission-time on low-complexity classification techniques. We illustrate the proposed classification-based system by performing MAC-application layer optimizations for video transmission over 802.11a wireless LANs. Specifically, we predict the optimal MAC retry limits for the various video packets and compare our results against both optimal and conventionally used ad-hoc cross-layer solutions. Our results indicate that considerable improvements can be obtained through the proposed cross-layer techniques relying on classification as opposed to optimized ad-hoc solutions. The improvements are especially important at high packet-loss rates (5% and higher), where deploying a judicious mixture of strategies at the various layers becomes essential. Furthermore, our proposed classification-based system can be easily modified to include other layers from the OSI stack during the cross-layer optimization.     

Adaptive content-and-deadline aware chunk scheduling in mesh-based P2P video streaming

In mesh-based Peer-to-Peer (P2P) live video streaming systems packet scheduling is an important factor in overall video playback quality. In mesh based P2P video streaming systems, each video sequence is divided into chunks, which are then distributed by multiple suppliers to the receivers. The suppliers need to be coordinated by the receiver through specifying a transmission schedule for each of them. Many previous studies on scheduling of P2P streaming tend to mainly focus on networking issues which strongly depend on a particular P2P architecture such as tree or mesh. These algorithms suffer from some design issues: 1) they are too complex to deploy, 2) they do not take video characteristics into account and 3) they do not have sender-side transmission policy. To address all three of these problems, we propose a new chunk scheduling scheme which consists of two parts: i) receiver-side scheduler and ii) sender-side transmission order scheme. The proposed receiver-side scheduler considers the contribution level of each video frame as well as the frame’s urgency in order to define a priority for each video frame. It attempts to request frames with highest priority from peers which can deliver them in a shorter time. We also design a new chunk transmission order scheme that decides which requested chunk will be sent out first based on its importance to the requesting neighbor. Our simulation results show that the proposed scheduling scheme improves the overall quality of the perceived video in mesh-based P2P video streaming architectures substantially.     

Video Packet Selection and Scheduling for Multipath Streaming

This paper addresses the problem of choosing the best streaming policy for distortion optimal multipath video delivery, under network bandwidth and playback delay constraints. The streaming policy consists in a joint selection of the network path and of the video packets to be transmitted, along with their sending time. A simple streaming model is introduced, which takes into account the video packet importance, and the dependencies between packets. A careful timing analysis allows to compute the quality perceived by the receiver for a constrained playback delay, as a function of the streaming policy. We derive an optimization problem based on a video abstraction model, under the assumption that the server knows, or can predict accurately the state of the network. A detailed analysis of constrained multipath streaming systems provides helpful insights to design an efficient branch and bound algorithm that finds the optimal streaming strategy. This solution allows to bound the performance of any scheduling strategy, but the complexity of the algorithm becomes rapidly intractable. We therefore propose a fast heuristic-based algorithm, built on load-balancing principles. It allows to reach close to optimal performance with a polynomial time complexity. The algorithm is then adapted to live streaming scenarios, where the server has only a partial knowledge of the packet stream, and the channel bandwidth. Extensive simulations show that the proposed algorithm only induces a negligible distortion penalty compared to the optimal strategy, even when the optimization horizon is limited, or the rate estimation is not perfect. Simulation results also demonstrate that the proposed scheduling solution performs better than common scheduling algorithms, and therefore represents a very efficient low-complexity multipath streaming algorithm, for both stored and live video services     

A weighted segment-based caching algorithm for video streaming objects over heterogeneous networking environments

Traditional caching technology is not applicable to cache video streaming objects over heterogeneous networking environments. The popularity of mobile devices in the heterogeneous networking environments make the access of Internet content become a common phenomenon. To support different mobile devices in the heterogeneity networking environments, a transcoding proxy is used to transcode different versions of the streaming videos according to clients’ requests. In this paper, we propose a weighted caching replace strategy for video streaming objects over heterogeneous networking environments. A new caching algorithm with static weight transcoding graph and dynamic caching relation tree is introduced. The proposed algorithm is compared with LRU, LFU, CP and PF cache algorithms in three parts: hit ratio, byte hit ratio, and average transmission delay. Experimental results show that the proposed algorithm outperforms than traditional LRU, LFU, CP and PF cache algorithms.     

Efficient Streaming Packet Video Over Differentiated Services Networks

We investigate streaming video over Differentiated Services (Diffserv) networks that can provide a number of aggregated traffic classes with increasing quality guarantee. We propose a method to measure the loss impact of a video packet on the quality of the decoded video images. We show how the optimal Quality-of-Service (QoS) mapping from the video packets into a set of traffic classes depends on the loss rates of the classes and the pricing model, and we develop an algorithm to accurately find the optimal QoS mapping. The performance of our algorithm is evaluated through computer simulations and compares favorably to an existing algorithm.     

Packet loss in peer-to-peer video streaming over the Internet

Peer-to-peer streaming has recently gained attention as an effective solution to support large scale media streaming applications over the Internet. One of the main challenges of peer-to-peer video streaming is the cumulative impact of the Internet packet loss due to the decoding dependency of the compressed video frames. In this paper we study the impact of the Internet packet loss on the performance of peer-to-peer video streaming systems, and analyze the efficiency of various packet loss recovery policies in such systems. Our analytical and simulation results show how the Internet packet loss can affect the performance of peer- to-peer video streaming systems and how different packet loss recovery policies can be effective for such systems. Our analysis results give us some insights that can be used in designing efficient peer-to-peer video streaming systems.