Streaming Media Delivery Research Articles

Quality of Experience in ICN: Keep Your Low- Bitrate Close and High-Bitrate Closer

Recent studies into streaming media delivery suggest that performance gains from ubiquitous caching in Information-Centric Networks (ICN) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieving multimedia content. Bitrate adaptation mechanisms, that drive video streaming, clash with caching mechanisms in ways that affect users' Quality of Experience (QoE). Cache performance also diminishes as consumers dynamically select content encoded at different bitrates. In this article we use this evidence to draw a novel insight: in adaptive streaming over ICN, bitrates should be prioritized alongside popularity and hit rates. We build on this insight to propose RippleCache as a family of cache placement schemes that safeguard high-bitrate content at the edge and push low-bitrate content into the network core. Doing so reduces contention of cache resources, as well as congestion in the network. To validate RippleCache claims we construct two separate implementations. We design RippleClassic as a benchmark solution that optimizes content placement by maximizing a measure for ICNs shown to have high correlation with QoE. In addition, our lighter-weight RippleFinder is then re-designed with distributed execution for application in large-scale systems. RippleCache performance gains are reinforced by evaluations in NS-3 against state-of-the-art baseline approaches, using standard measures of QoE as defined by the DASH Industry Forum. Our results demonstrate that RippleClassic and RippleFinder deliver content that suffers less oscillation and rebuffering, all while achieving the highest levels of video quality; thus indicating overall improvements to QoE.

Dynamic Scene Change Detection in Video Coding

Video compression has become a source of different research studies. It is necessary in order to address channel bandwidth limitations and growing video demand, including digital libraries and streaming media delivery via the Internet. A video is a number of frames captured by a camera while a scene is a series of consecutive frames captured from a specific narrative viewpoint. To compress a video, firstly, the intra frames are separated from inter frames using scene change detection methods. Then, block-based motion estimation algorithms are used to eliminate the temporal redundancy between successive frames. This paper describes some scene change detection methods for use on the uncompressed video to detect scene types such as cut, dissolve, wipe, etc. Absolute Frame Difference (AFD), Mean Absolute Frame Differences (MAFD), Mean Histogram Absolute Frame Difference (MHAFD), and Maximum Gradient Value (MGV) techniques are adaptively tested on different video types to identify accurate scene change in both low and high object motion scenes. Test results show that the proposed approach (MHAFD) obtains a better accuracy, F1-scor measure of 100%, especially for cuts and gradual transitions (wipe) video types. Dissolve scene change is detected with a high precision of 100% (i.e., no false detection) with the (MAFD) detector. Besides, in terms of time complexity for analyzing all the video samples, the proposed method (MHAFD ) provides the best result compared to the selected detectors.

Report from the Standards Vice President: Standards for Streaming Media

<fig orientation="portrait" position="float" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <graphic orientation="portrait" position="float" xlink:href="lambs-2640078.tif"/> </fig> Every day my inbox is filled with industry newsletters and press releases talking about the next wave of streaming media delivery. There are traditional broadcasters who have added a streaming service to their offering, dedicated “Over the Top” content distributors, and traditional cable and satellite providers, to mention just a few. Streaming delivery of media direct to the viewer at home on large screen TVs and computer screens, as well as on mobile devices of all types has become commonplace in our media-hungry world.

Insights on Media Streaming Progress Using BitTorrent-Like Protocols for On-Demand Streaming

This paper develops analytical models that characterize the behavior of on-demand stored media content delivery using BitTorrent-like protocols. The models capture the effects of different piece selection policies, including Rarest-First, two variants of In-Order, and two probabilistic policies (Portion and Zipf). Our models provide insight into system behavior and help explain the sluggishness of the system with In-Order streaming. We use the models to compare different retrieval policies across a wide range of system parameters, including peer arrival rate, upload/ download bandwidth, and seed residence time. We also provide quantitative results on the startup delays and retrieval times for streaming media delivery. Our results provide insights into the design tradeoffs for on-demand media streaming in peer-to-peer networks. Finally, the models are validated using simulations.

Streaming Media Caching Model Based on Knapsack Problem

The dominant traffic on the Internet has changed from text and graphics based Web content to more information-rich streaming media content, such as audio and video. With the dramatic increase of network bandwidth and the advancement of technologies on media authoring, encoding, and distribution, media traffic on the Internet has increased explosively and now accounts for the majority of traffic volume. Modern Internet streaming services have utilized various techniques to improve the quality of streaming media delivery. Proxy server is one of the main solutions used to improve Internet QoS, especially for the QoS of streaming media. Replacement algorithm optimization is the core of caching model research. However, existing techniques for caching text and image resources are not appropriate for the rapidly growing number of continuous media streams. Based on the concept of hit ratio, this paper makes use of 0-1 knapsack problem to set up a hit ratio model of proxy cache, by use of which a proxy cache policy is presented. As compared with the classical dynamic streaming scheduling strategies, the proposed algorithm is shown that it can make full use of space of proxy cache, and also get a higher hit ratio.

Analysis of bittorrent-like protocols for on-demand stored media streaming

This paper develops analytic models that characterize the behavior of on-demand stored media content delivery using BitTorrent-like protocols. The models capture the effects of different piece selection policies, including Rarest-First and two variants of In-Order. Our models provide insight into transient and steady-state system behavior, and help explain the sluggishness of the system with strict In-Order streaming. We use the models to compare different retrieval policies across a wide range of system parameters, including peer arrival rate, upload/download bandwidth, and seed residence time. We also provide quantitative results on the startup delays and retrieval times for streaming media delivery. Our results provide insights into the optimal design of peer-to-peer networks for on-demand media streaming.

Streaming of Continuous Media for Distance Education Systems

Distance education created new challenges regarding the delivery of large size isochronous continuous streaming media (SM) objects. In this paper, we consider the design of a framework for customized SM presentations, where each presentation consists of a number of SM objects that should be retrieved and displayed to the user in a coherent fashion. We describe a retrieval optimizer (Prime) that captures the flexibilities and requirements imposed by the user query, user profile, and session profile. Then, it determines how this query script should be imposed against the continuous media (CM) server to reduce contention. We also provide a cost model to evaluate each proposed plan. Finally, we explain the role of memory buffering in alleviating the server bandwidth fragmentation problem. Our preliminary experimental results show the feasibility and effectiveness of our proposed model and techniques in generating near optimal retrieval.

VIRTUAL ACTIVE NETWORK FOR LIVE STREAMING MEDIA DELIVERY

The large amount of bandwidth and other resources required to deliver streaming media limits the number of concurrent users. We propose a virtual active network (VAN) architecture for streaming media data delivery over wide area networks. In the proposed architecture, cooperating proxies support multiplexing and delivery of live streaming media. The hierarchical delivery structure is dynamically adjusted based on user population distribution, usage patterns, and network conditions. The proposed system architecture provides (1) reliable and high quality live streaming media delivery; (2) lower server resource requirements at the content provider sites; (3) reduced inter-ISP traffic; (4) application level routing for rapid deployment; and (5) cost-effective media data delivery. To deal with one characteristics of live broadcasting events, burst traffic at the beginning of the events, our system features a unique function that dynamically clusters multiple proxy servers to form a server farm to handle a large number of user login events when needed. Experimental results show that the proposed VAN architecture consistently delivers reliable live data streams using resources within 10 percent of the theoretically possible lower bound. The experimental results also show the effectiveness of our load balance algorithms to handle various user patterns, server capacity, and network congestion events.

Streams of Consciousness

SUMMARY Streaming media delivery on the Web has become a ubiquitous service expected by many of today's library patrons. Libraries can offer selection, preservation, dissemination, and production of streaming media content. The Spencer S. Eccles Health Sciences Library has offered its patrons streaming media and on-demand video services since 2000. A mobile video broadcasting unit was designed and implemented to deliver live broadcasts of lectures and special events. Educational video digitization, production, and cataloging services were offered. Challenges and successes with this service are reported.

Design and evaluation of a scalable and reliable P2P assisted proxy for on-demand streaming media delivery

To efficiently deliver streaming media, researchers have developed technical solutions that fall into three categories, each of which has its merits and limitations. Infrastructure-based CDNs with dedicated network bandwidths and hardware supports can provide high-quality streaming services, but at a high cost. Server-based proxies are cost-effective but not scalable due to the limited proxy capacity in storage and bandwidth, and its centralized control also brings a single point of failure. Client-based P2P networks are scalable, but do not guarantee high-quality, streaming service due to the transient nature of peers. To address these limitations, we present a novel and efficient design of a scalable and reliable media proxy system assisted by P2P networks, called PROP. In the PROP system, the clients' machines in an intranet are self-organized into a structured P2P system to provide a large media storage and to actively participate in the streaming media delivery, where the proxy is also embedded as an important member to ensure the quality of streaming service. The coordination and collaboration in the system are efficiently conducted by our P2P management structure and replacement policies. Our system has the following merits: 1) It addresses both the scalability problem in centralized proxy systems and the unreliable service concern by only relying on the P2P sharing of clients. 2) The proposed content locating scheme can timely serve the demanded media data and fairly dispatch media streaming tasks in appropriate granularity across the system. 3) Based on the modeling and analysis, we propose global replacement policies for proxy and clients, which well balance the demand and supply of streaming data in the system, achieving a high utilization of peers' cache. We have comparatively evaluated our system through trace-driven simulations with synthetic workloads and with a real-life workload extracted from the media server logs in an enterprise network, which shows our design significantly improves the quality of media streaming and the system scalability.

Real-time scheduling based on imprecise computation for scalable streaming media system over the Internet

This paper proposes a performance metrics and a real-time scheduling algorithm based on imprecise computation workload model for delivery of scalable streaming media, which can be adapted to network status and QoS requirement over the best-effort Internet. The scheduling task of a scalable streaming media is partitioned into two subtasks: the mandatory subtask for the base layer and the optional subtask for the enhancement layers. The imprecise computation workload model and real-time scheduling algorithm provide scheduling flexibility by trading off video quality reconstructed in client to meet the playback deadline. Thus, the better usage of available bandwidth and smoother playback are achieved.

Caching Strategies in Transcoding-Enabled Proxy Systems for Streaming Media Distribution Networks

With the wide availability of high-speed network access, we are experiencing high quality streaming media delivery over the Internet. The emergence of ubiquitous computing enables mobile users to access the Internet with their laptops, PDAs, or even cell phones. When nomadic users connect to the network via wireless links or phone lines, high quality video transfer can be problematic due to long delay or size mismatch between the application display and the screen. Our proposed solution to this problem is to enable network proxies with the transcoding capability, and hence provide different, appropriate video quality to different network environment. The proxies in our transcoding-enabled caching (TeC) system perform transcoding as well as caching for efficient rich media delivery to heterogeneous network users. This design choice allows us to perform content adaptation at the network edges. We propose three different TeC caching strategies. We describe each algorithm and discuss its merits and shortcomings. We also study how the user access pattern affects the performance of TeC caching algorithms and compare them with other approaches. We evaluate TeC performance by conducting two types of simulation. Our first experiment uses synthesized traces while the other uses real traces derived from an enterprise media server logs. The results indicate that compared with the traditional network caches, with marginal transcoding load, TeC improves the cache effectiveness, decreases the user-perceived latency, and reduces the traffic between the proxy and the content origin server.

Network-aware partial caching for Internet streaming media

The delivery of multimedia over the Internet is affected by adverse network conditions such as high packet loss rate and long delay. This paper aims at mitigating such effects by leveraging client-side caching proxies. We present a novel cache architecture and associated cache management algorithms that turn edge caches into accelerators of streaming media delivery. This architecture allows partial caching of media objects and joint delivery from caches and origin servers. Most importantly, the caching algorithms are both network-aware and stream-aware; they take into account the popularity of streaming media objects, their bit rate requirements, and the available bandwidth between clients and servers. Using Internet bandwidth models derived from proxy cache logs and measured over real Internet paths, we have conducted extensive simulations to evaluate the performance of various cache management algorithms. Our experiments demonstrate that network-aware caching algorithms can significantly reduce startup delay and improve stream quality. Our experiments also show that partial caching is particularly effective when bandwidth variability is not very high.

G ISMO

This paper presents a tool called G ISMO (Generator of Internet Streaming Media Objects and workloads). G ISMO enables the specification of a number of streaming media access characteristics, including object popularity, temporal correlation of request, seasonal access patterns, user session durations, user inter-activity times, and variable bit-rate (VBR) self-similarity and marginal distributions. The embodiment of these characteristics in G ISMO enables the generation of realistic and scalable request streams for use in the benchmarking and comparative evaluation of Internet streaming media delivery techniques. To demonstrate the usefulness of G ISMO , we present a case study that shows the importance of various workload characteristics in determining the effectiveness of proxy caching and server patching techniques in reducing bandwidth requirements.

Video coding for streaming media delivery on the Internet

We provide an overview of an architecture of today's Internet streaming media delivery networks and describe various problems that such systems pose with regard to video coding. We demonstrate that based on the distribution model (live or on-demand), the type of the network delivery mechanism (unicast versus multicast), and optimization criteria associated with particular segments of the network (e.g., minimization of distortion for a given connection rate, minimization of traffic in the dedicated delivery network, etc.), it is possible to identify several models of communication that may require different treatment from both source and channel coding perspectives. We explain how some of these problems can be addressed using a conventional framework of temporal motion-compensated, transform-based video compression algorithm, supported by appropriate channel-adaptation mechanisms in client and server components of a streaming media system. Most of these techniques have already been implemented in RealNetworks(R) RealSystem(R) 8 and its RealVideo(R) 8 codec, which we use throughout the paper to illustrate our results.