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

Proxy Support for HTTP Adaptive Streaming

Not long ago streaming video over the Internet included only short clips of low quality video. Now the possibilities seem endless as professional productions are made available in high definition. This explosion of growth is the result of several factors, such as increasing network performance, advancements in video encoding technology, improvements to video streaming techniques, and a growing number of devices capable of handling video. However, despite the improvements to Internet video streaming this paradigm is still evolving. HTTP adaptive streaming involves encoding a video at multiple quality levels then dividing those quality levels into small chunks. The player can then determine which quality level to retrieve the next chunk from in order to optimize video playback when considering the underlying network conditions. This thesis first presents an experimental framework that allows for adaptive streaming players to be analyzed and evaluated. Evaluation is beneficial because there are several concerns with the adaptive video streaming ecosystem such as achieving a high video playback quality while also ensuring stable playback quality. The primary contribution of this thesis is the evaluation of prefetching by a proxy server as a means to improve streaming performance. This work considers an implementation of a proxy server that is functional with the extremely popular Netflix streaming service, and it is evaluated using two Netflix players. The results show its potential to improve video streaming performance in several scenarios. It effectively increases the buffer capacity of the player as chunks can be prefetched in advance of the player's request then stored on the proxy to be quickly delivered once requested. This allows for degradation in network conditions to be hidden from the player while the proxy serves prefetched data, preventing a reduction to the video quality as a result of an overreaction by the player. Further, the proxy can reduce the impact of the bottleneck in the network, achieving higher throughput by utilizing parallel connections to the server

On the merits of SVC-based HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) is quickly becoming the dominant type of video streaming in Over-The-Top multimedia services. HAS content is temporally segmented and each segment is offered in different video qualities to the client. It enables a video client to dynamically adapt the consumed video quality to match with the capabilities of the network and/or the client's device. As such, the use of HAS allows a service provider to offer video streaming over heterogeneous networks and to heterogeneous devices. Traditionally, the H. 264/AVC video codec is used for encoding the HAS content: for each offered video quality, a separate AVC video file is encoded. Obviously, this leads to a considerable storage redundancy at the video server as each video is available in a multitude of qualities. The recent Scalable Video Codec (SVC) extension of H. 264/AVC allows encoding a video into different quality layers: by dowloading one or more additional layers, the video quality can be improved. While this leads to an immediate reduction of required storage at the video server, the impact of using SVC-based HAS on the network and perceived quality by the user are less obvious. In this article, we characterize the performance of AVC- and SVC-based HAS in terms of perceived video quality, network load and client characteristics, with the goal of identifying advantages and disadvantages of both options

Quality of experience-centric management of adaptive video streaming services : status and challenges

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years

Evaluation of HTTP/DASH Adaptation Algorithms on Vehicular Networks

Video streaming currently accounts for the majority of Internet traffic. One factor that enables video streaming is HTTP Adaptive Streaming (HAS), that allows the users to stream video using a bit rate that closely matches the available bandwidth from the server to the client. MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a widely used standard, that allows the clients to select the resolution to download based on their own estimations. The algorithm for determining the next segment in a DASH stream is not partof the standard, but it is an important factor in the resulting playback quality. Nowadays vehicles are increasingly equipped with mobile communication devices, and in-vehicle multimedia entertainment systems. In this paper, we evaluate the performance of various DASH adaptation algorithms over a vehicular network. We present detailed simulation results highlighting the advantages and disadvantages of various adaptation algorithms in delivering video content to vehicular users, and we show how the different adaptation algorithms perform in terms of throughput, playback interruption time, and number of interruptions

Building Efficient Software to Support Content Delivery Services

Many content delivery services use key components such as web servers, databases, and key-value stores to serve content over the Internet. These services, and their component systems, face unique modern challenges. Services now operate at massive scale, serving large files to wide user-bases. Additionally, resource contention is more prevalent than ever due to large file sizes, cloud-hosted and collocated services, and the use of resource-intensive features like content encryption. Existing systems have difficulty adapting to these challenges while still performing efficiently. For instance, streaming video web servers work well with small data, but struggle to service large, concurrent requests from disk. Our goal is to demonstrate how software can be augmented or replaced to help improve the performance and efficiency of select components of content delivery services. We first introduce Libception, a system designed to help improve disk throughput for web servers that process numerous concurrent disk requests for large content. By using serialization and aggressive prefetching, Libception improves the throughput of the Apache and nginx web servers by a factor of 2 on FreeBSD and 2.5 on Linux when serving HTTP streaming video content. Notably, this improvement is achieved without changing the source code of either web server. We additionally show that Libception's benefits translate into performance gains for other workloads, reducing the runtime of a microbenchmark using the diff utility by 50% (again without modifying the application's source code). We next implement Nessie, a distributed, RDMA-based, in-memory key-value store. Nessie decouples data from indexing metadata, and its protocol only consumes CPU on servers that initiate operations. This design makes Nessie resilient against CPU interference, allows it to perform well with large data values, and conserves energy during periods of non-peak load. We find that Nessie doubles throughput versus other approaches when CPU contention is introduced, and has 70% higher throughput when managing large data in write-oriented workloads. It also provides 41% power savings (over idle power consumption) versus other approaches when system load is at 20% of peak throughput. Finally, we develop RocketStreams, a framework which facilitates the dissemination of live streaming video. RocketStreams exposes an easy-to-use API to applications, obviating the need for services to manually implement complicated data management and networking code. RocketStreams' TCP-based dissemination compares favourably to an alternative solution, reducing CPU utilization on delivery nodes by 54% and increasing viewer throughput by 27% versus the Redis data store. Additionally, when RDMA-enabled hardware is available, RocketStreams provides RDMA-based dissemination which further increases overall performance, decreasing CPU utilization by 95% and increasing concurrent viewer throughput by 55% versus Redis