Video loss recovery with FEC and stream replication
Packet loss is inevitable in video multicast. In this paper, we propose and study an effective feedback-free loss recovery scheme for layered video which combines forward error correction (FEC) and stream replication. In our scheme, the server multicasts the video in parallel with FEC packets and a number of replicated delayed (ReD) version of the stream. Receivers autonomously and dynamically join the FEC and ReD streams to repair their losses. On the server side, we analyze and optimize the number of replicated streams and FEC packets to meet a certain residual loss requirement (i.e., error after correction). On the receiver side, we analyze the optimal combination of FEC and ReD packets to minimize its loss. We also present a fast yet accurate approximation algorithm for receiver to make such decision. We show that FEC combined with merely one or two replicated streams can effectively reduce the residual error rate (by as much as 50%) as compared with pure FEC or replication alone. Both subjective and objective video measures confirm that our recovery scheme achieves much better visual quality.

title: A new motion compensation approach for error resilient video coding
abstract: Multihypothesis motion-compensated prediction (MHMCP) can be used as an error resilience technique for video coding. Motivated by MHMCP, we propose a new error resilience approach named alternative motion-compensated prediction (AMCP), where two-hypothesis and one-hypothesis predictions are alternatively used with some mechanism. Both theory and simulation results show that in case of one frame loss, the expected converged error using AMCP is smaller than that using two-hypothesis MCP.

title: Distributed servers architecture for networked video services
abstract: In an on-demand video system, the video repository generally has limited streaming capacities and may be far from the users. In order to achieve higher user capacity and lower network transmission cost, distributed servers architecture can be used, in which multiple local servers are placed close to user pools and, according to their local demands, dynamically cache the contents streamed from the repository. We study a number of caching schemes as applied in the local servers depending on whether the repository is able to multicast movie contents to the local servers or not, and whether the local servers can exchange their cached contents among themselves or not. Our caching schemes keep a circular buffer of data for the movie requested, and hence movies are partially cached. By adjusting the size of the buffer, such caching is able to achieve better tradeoff between network channels and local storage as compared to the traditional caching in which a movie is treated as an entity. For each caching scheme, we study the tradeoff between the local storage and the network channels, and address how the total cost of the system can be minimized by appropriately sizing the buffer. As compared to a number of traditional operations (request batching and multicasting, true-VOD, etc.), we show that distributed servers architecture is able to achieve much lower system cost to offer on-demand video services.

title: Improved refinement search for H.263 to H.264/AVC transcoding based on the minimum cost tendency search
abstract: An improved refinement search method for transcoding from H.263 to H.264/AVC is proposed in this paper. Many existing motion re-estimation methods refine the input motion vector (MV) with a small search range, which is usually input MV biased. Motion estimation (ME) in H.263 usually does not consider the rate required for coding the MV, and hence, the input MV may incur a large cost in H.264/AVC. To overcome this problem, we introduce a refinement search method, called minimum cost tendency search (MCTS), which takes the difference between the cost functions for ME in H.263 and H.264/AVC into consideration. The input MV and the predictor MV are used as two anchor points. The proposed MCTS starts searching from the anchor point with a higher cost to another. Finally, the best point is chosen as the center for further refinement. The performance of MCTS is evaluated by comparing with full search, FME in JM software and refinement scheme using small diamond pattern around the input MV (RSD). Experimental results show the proposed MCTS performs more stable than FME and RSD over a wide range of output video quality

title: Fine-grained device management in an interactive media server
abstract: The use of interactive media has already gained considerable popularity. Interactivity gives viewers VCR controls like slow-motion, pause, fast-forward, and instant replay. However, traditional server-based or client-based approaches for supporting interactivity either consume too much network bandwidth or require large client buffering; and hence they are economically unattractive. We propose the architecture and design of an interactive media proxy (IMP) server that transforms noninteractive broadcast or multicast streams into interactive ones for servicing a large number of end users. For IMP to work cost-effectively, it must carefully manage its storage devices, which are needed for caching voluminous media data. In this regard, we propose a fine-grained device management strategy consisting of three complementary components: disk profiler, data placement, and IO scheduler. Through quantitative analysis and experiments, we show that these fine-grained strategies considerably improve device throughput under various workload scenarios.

title: Modeling and Dimensioning Hierarchical Storage Systems for Low-Delay Video Services
abstract: In order to cost-effectively accommodate a large number of titles in a video system, a hierarchical storage system can be used. In this system, not-so-popular video files are stored in a tertiary level such as a disk/tape library. These files are transferred, or "staged," to a secondary level composed of magnetic disks before being streamed to the users. This system overcomes the current limitations in using disk/tape libraries to stream videos and resolves the bandwidth difference between staging and streaming. We present, via analysis, a model of the system and determine the minimum storage and bandwidth required, at each level, to meet a given user delay goal. We also analyze a number of system operations pertaining to whether or not a file is played while it is being staged (i.e., stage-streaming) and whether or not the displayed segments are deleted (i.e., trail-deletion). We show that stage-streaming and trail-deletion can achieve substantially lower bandwidth and storage requirements. In order to further increase the streaming and storage scalability, a distributed storage system can be used where multiple local servers are put close to user pools and get their files from one of the libraries through a network. We extend the models developed to such a system and specify the resource requirements to meet a given delay goal.

title: Extracting Causal Knowledge from a Medical Database Using Graphical Patterns
abstract: This paper reports the first part of a project that aims to develop a knowledge extraction and knowledge discovery system that extracts causal knowledge from textual databases. In this initial study, we develop a method to identify and extract cause-effect information that is explicitly expressed in medical abstracts in the Medline database. A set of graphical patterns were constructed that indicate the presence of a causal relation in sentences, and which part of the sentence represents the cause and which part represents the effect. The patterns are matched with the syntactic parse trees of sentences, and the parts of the parse tree that match with the slots in the patterns are extracted as the cause or the effect.

title: Mapping XML Schema to Relations Using Genetic Algorithm
abstract: As web-applications grow in number and complexity, there is a need for efficient mappings from XML schemas to the flat relational tables so that existing functions in relational database systems can be utilized. However, many of the existing mapping methods, such as the model-based or the structure-based methods, do not exploit query history for better query performance. In this paper, we propose the use of genetic algorithm (GA) in a cost-based approach for converting a XML schema to relational tables. By formulating the mapping problem as a cost optimization task with respect to a set of weighted frequent queries, we can obtain an efficient mapping that minimizes the queries execution time. In our experiments, we show that the mapping obtained by GA is superior to other non-cost-based approaches. In particular, the GA approach has out-performed the greedy heuristic in the browsing queries where the accessed attributes are many and scattered.

title: A solid modeling library for the World Wide Web
abstract: A library of functions for the creation, manipulation, and viewing of solid models was created for deployment on the World Wide Web. The core data structure was based on the halfedge boundary-representation data structure proposed by M. Mantyla but much functionality had been added. It provided an alternate, constructive-solid-geometry interface to the core data structure. It was initially implemented in C and subsequently re-implemented in Java. An interface to the STEP standard and various applications such as a simple solid modeling applet, a conic illustrator applet, and a virtual furniture showroom applet had been built. It is being extended to support multiple simultaneous users as part of a prototype WWW-based tele-processing manufacturing system.

title: Hypertext-assisted video indexing and content-based retrieval
abstract: An effective approach has been designed for the construction of on-line educational video systems. We have made use of the assistance of HTML text for accurate video parsing and complete content extraction. A video system with segmentation/indexing module and browsing/query modules are implemented to demonstrate the idea.

title: A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
abstract: The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS).  extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches.