8 research outputs found
Numeric Analysis for Relationship-Aware Scalable Streaming Scheme
Frequent packet loss of media data is a critical problem that degrades the quality of streaming services over mobile networks. Packet loss invalidates frames containing lost packets and other related frames at the same time. Indirect loss caused by losing packets decreases the quality of streaming. A scalable streaming service can decrease the amount of dropped multimedia resulting from a single packet loss. Content providers typically divide one large media stream into several layers through a scalable streaming service and then provide each scalable layer to the user depending on the mobile network. Also, a scalable streaming service makes it possible to decode partial multimedia data depending on the relationship between frames and layers. Therefore, a scalable streaming service provides a way to decrease the wasted multimedia data when one packet is lost. However, the hierarchical structure between frames and layers of scalable streams determines the service quality of the scalable streaming service. Even if whole packets of layers are transmitted successfully, they cannot be decoded as a result of the absence of reference frames and layers. Therefore, the complicated relationship between frames and layers in a scalable stream increases the volume of abandoned layers. For providing a high-quality scalable streaming service, we choose a proper relationship between scalable layers as well as the amount of transmitted multimedia data depending on the network situation. We prove that a simple scalable scheme outperforms a complicated scheme in an error-prone network. We suggest an adaptive set-top box (AdaptiveSTB) to lower the dependency between scalable layers in a scalable stream. Also, we provide a numerical model to obtain the indirect loss of multimedia data and apply it to various multimedia streams. Our AdaptiveSTB enhances the quality of a scalable streaming service by removing indirect loss
ActiveSTB: an efficient wireless resource manager in home networks
The rapid growth of new wireless and mobile devices accessing the internet has
led to an increase in the demand for multimedia streaming services. These home-based
wireless connections require efficient distribution of shared network resources which is a
major concern for the transport of stored video. In our study, a set-top box is the access
point between the internet and a home network. Our main goal is to design a set-top box
capable of performing network flow control in a home network and capable of quality
adaptation of the delivered stream quality to the available bandwidth. To achieve our
main goal, estimating the available bandwidth quickly and precisely is the first task in
the decision of streaming rates of layered and scalable multimedia services. We present
a novel bandwidth estimation method called IdleGap that uses the NAV (Network
Allocation Vector) information in the wireless LAN. We will design a new set-top box
that will implement IdleGap and perform buffering and quality adaptation to a wireless
network based on the IdleGap’s bandwidth estimate. We use a network simulation tool
called NS-2 to evaluate IdleGap and our ActiveSTB compared to traditional STBs. We
performed several tests simulating network conditions over various ranges of cross
traffic with different error rates and observation times. Our simulation results reveal
how IdleGap accurately estimates the available bandwidth for all ranges of cross traffic
(100Kbps ~ 1Mbps) with a very short observation time (10 seconds). Test results also
reveal how our novel ActiveSTB outperforms traditional STBs and provides good QoS
to the end-user by reducing latency and excess bandwidth consumption
Building Internet caching systems for streaming media delivery
The proxy has been widely and successfully used to cache the static Web objects fetched by a client so that the subsequent clients requesting the same Web objects can be served directly from the proxy instead of other sources faraway, thus reducing the server\u27s load, the network traffic and the client response time. However, with the dramatic increase of streaming media objects emerging on the Internet, the existing proxy cannot efficiently deliver them due to their large sizes and client real time requirements.;In this dissertation, we design, implement, and evaluate cost-effective and high performance proxy-based Internet caching systems for streaming media delivery. Addressing the conflicting performance objectives for streaming media delivery, we first propose an efficient segment-based streaming media proxy system model. This model has guided us to design a practical streaming proxy, called Hyper-Proxy, aiming at delivering the streaming media data to clients with minimum playback jitter and a small startup latency, while achieving high caching performance. Second, we have implemented Hyper-Proxy by leveraging the existing Internet infrastructure. Hyper-Proxy enables the streaming service on the common Web servers. The evaluation of Hyper-Proxy on the global Internet environment and the local network environment shows it can provide satisfying streaming performance to clients while maintaining a good cache performance. Finally, to further improve the streaming delivery efficiency, we propose a group of the Shared Running Buffers (SRB) based proxy caching techniques to effectively utilize proxy\u27s memory. SRB algorithms can significantly reduce the media server/proxy\u27s load and network traffic and relieve the bottlenecks of the disk bandwidth and the network bandwidth.;The contributions of this dissertation are threefold: (1) we have studied several critical performance trade-offs and provided insights into Internet media content caching and delivery. Our understanding further leads us to establish an effective streaming system optimization model; (2) we have designed and evaluated several efficient algorithms to support Internet streaming content delivery, including segment caching, segment prefetching, and memory locality exploitation for streaming; (3) having addressed several system challenges, we have successfully implemented a real streaming proxy system and deployed it in a large industrial enterprise
Adaptive Resource Management Schemes for Web Services
Web cluster systems provide cost-effective solutions when scalable and reliable
web services are required. However, as the number of servers in web cluster systems
increase, web cluster systems incur long and unpredictable delays to manage servers.
This study presents the efficient management schemes for web cluster systems.
First of all, we propose an efficient request distribution scheme in web cluster
systems. Distributor-based systems forward user requests to a balanced set of waiting
servers in complete transparency to the users. The policy employed in forwarding
requests from the frontend distributor to the backend servers plays an important
role in the overall system performance. In this study, we present a proactive request
distribution (ProRD) to provide an intelligent distribution at the distributor.
Second, we propose the heuristic memory management schemes through a web
prefetching scheme. For this study, we design a Double Prediction-by-Partial-Match
Scheme (DPS) that can be adapted to the modern web frameworks. In addition, we
present an Adaptive Rate Controller (ARC) to determine the prefetch rate depending
on the memory status dynamically. For evaluating the prefetch gain in a server node,
we implement an Apache module.
Lastly, we design an adaptive web streaming system in wireless networks. The
rapid growth of new wireless and mobile devices accessing the internet has contributed
to a whole new level of heterogeneity in web streaming systems. Particularly, in-home
networks have also increased in heterogeneity by using various devices such as laptops, cell phone and PDAs. In our study, a set-top box(STB) is the access pointer between
the internet and a home network. We design an ActiveSTB which has a capability of
buffering and quality adaptation based on the estimation for the available bandwidth
in the wireless LAN