HTTP adaptive streaming with media fragment URIs

HTTP adaptive streaming was introduced with the general idea that user agents interpret a manifest file (describing different representations and segments of the media); where-after they retrieve the media content using sequential HTTP progressive download operations. MPEG started with the standardization of an HTTP streaming protocol, defining the structure and semantics of a manifest file and additional restrictions and extensions for container formats. At the same time, W3C is working on a specification for addressing media fragments on the Web using Uniform Resource Identifiers. The latter not only defines the URI syntax for media fragment identifiers but also the protocol for retrieving media fragments over HTTP. In this paper, we elaborate on the role of Media Fragment URIs within HTTP adaptive streaming scenarios. First, we elaborate on how different media representations can be addressed by means of Media Fragment URIs, by using track fragments. Additionally, we illustrate how HTTP adaptive streaming is realized relying on the Media Fragments URI retrieval protocol. To validate the presented ideas, we implemented Apple's HTTP Live streaming technique using Media Fragment URI

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

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

Cumulative Quality Modeling for HTTP Adaptive Streaming

Thanks to the abundance of Web platforms and broadband connections, HTTP Adaptive Streaming has become the de facto choice for multimedia delivery nowadays. However, the visual quality of adaptive video streaming may fluctuate strongly during a session due to bandwidth fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this paper, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify three important components of the cumulative quality model, namely the minimum window quality, the last window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation. The source code of the proposed model has been made available to the public at https://github.com/TranHuyen1191/CQM

Towards Smooth and High-Quality Bitrate Adaptation for HTTP Adaptive Streaming

Although HTTP adaptive streaming has been well documented for the cost-effective delivery of video streaming, it is still a great challenge to play back video smoothly with high quality under the fluctuating network conditions. In this paper, we proposed a novel bitrate adaptation algorithm for HTTP adaptive streaming. Our algorithm employed two approaches for throughput estimation and bitrate selection, which was evaluated on our testbed (a fully functional HTTP Live Streaming system) over a network, emulated using DummyNet. First, the throughput estimation method, based on the prediction of the difference between the estimated and instantaneous throughputs, was observed to respond smoothly to short-term fluctuations and rapidly to large fluctuations. Second, the bitrate selection algorithm, based on piecewise functions to define the variation range of the current bitrate, was found to result in smoother changes in quality with a higher average quality. The results of our experiments demonstrated the prospects of our bitrate adaptation algorithm for HTTP adaptive streaming

Evaluation of Q-Learning approach for HTTP adaptive streaming

We propose a Q-Learning-based algorithm for an HTTP Adaptive Streaming (HAS) Client that maximizes the perceived quality, taking into account the relation between the estimated bandwidth and the qualities and penalizing the freezes. The results will show that it produces an optimal control as other approaches do, but keeping the adaptivenes