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SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos 时间:2017-09-14 15:09 来源:未知作者:admin 点击: 次 Abstract:Recently, various adaptation methods have been proposed to cope with throughput fluctuations in HTTP adaptive streaming (HAS). However, these methods have mostly focused on constant bitrate (CBR) videos. Moreover, most of them are qualitative in the sense that performance metrics could only be obtained after a streaming session. In this paper, we propose a new adaptation method for streaming variable bitrate (VBR) videos using stochastic dynamic programming (SDP). With this approach, the system should have a probabilistic characterization along with the definition of a cost function that is minimized by a control strategy. Our solution is based on a new statistical model where the future streaming performance is directly related to the past bandwidth statistics. We develop mathematical models to predict and develop simulation models to measure the average performance of the adaptation policy. The experimental results show that the prediction models can provide accurate performance prediction which is useful in planning adaptation policy and that our proposed adaptation method outperforms the existing ones in terms of average quality and average quality switch.
1. Introduction
Nowadays, video services are increasingly popular on the Internet. According to a recent study and forecast [1], global Internet video traffic will be 80% of the entire consumer Internet traffic in 2019. Besides, HTTP protocol has become a cost-effective solution for video streaming thanks to the abundance of Web platforms and broadband connections [2, 3]. Furthermore, for interoperability of HTTP streaming in the industry, ISO/IEC MPEG has developed “Dynamic Adaptive Streaming over HTTP” (DASH) [4] as the first standard for video streaming over HTTP.
DASH requires a video to be available in multiple bitrates and split into small segments each containing a few seconds of playtime. Based on the current network conditions and terminal capacity, the client can adaptively decide a suitable data rate so that stalling is avoided and the available bandwidth is best possibly utilized. If the video is encoded in only one bitrate, either the bitrate is smaller than the available bandwidth resulting in a smooth playback but sparing resources which could be utilized for a better video quality, or the video bitrate is higher than the available bandwidth leading to video stalling. Thus, DASH enables service providers to improve resource utilization and quality of experience (QoE).
So far, existing studies have proposed simple heuristics for adapting video at the client. These heuristics can be divided into two types, buffer-based methods and throughput-based methods. The purpose of buffer-based methods is to maintain the stability of the buffer within a certain range to ensure continuous video playback. However, when the bandwidth is drastically reduced, the buffer-based methods may cause sudden change of bitrate [5–8]. Meanwhile, throughput-based methods adaptively decide version based on the estimated throughput. These methods are generally able to react quickly to the throughput variations; the streaming quality, however, may be unstable [9].
Recently, several Markov decision-based methods have been proposed to optimize decision making for the streaming client under time-varying network conditions. However, these existing methods mostly focus on constant bitrate (CBR) videos. The authors in [10] are the first to propose an adaptation algorithm in which stochastic dynamic programming (SDP) is employed to find optimal decision policies when streaming VBR videos. The segment requests are ruled by the policies which map a control parameter to every possible state of the system; however, it is limited to videos with weak bitrate fluctuations. To the extent of the authors’ knowledge, in the context of adaptive streaming, there have not been any adaptive streaming methods that could () support variable bitrate (VBR) videos with strong bitrate fluctuations and () predict the streaming performance with different streaming settings in order to select the optimal one.
In this paper, we tackle these challenges by proposing an adaptation method using stochastic dynamic programming. Firstly, we discretize a system including data throughput, buffer level, and bitrate of a VBR video to form the system states. Secondly, we define a cost function that takes into account parameters that affect the subjective perceptual quality of users. In the cost function, the weights are assigned to the difference between data throughput and the bitrate of the next segment, the variance of the buffer from its optimal value, and the quality switch of the video. Finally, we construct an infinite horizon problem (IHP) and solve it to find the optimal policies for all system states. The role of a policy is mapping the control parameter (i.e., the version of the video) to every possible state of the system. This paper is an extended work of our preliminary study in [11]. The extension in this work is multifold. First, we predicted the CDF of the requested versions in a streaming session, so the maximum version could be decided for the streaming session. Second, we predicted CDF of the buffer levels to know the variance of the buffer level under the fluctuations of the network. Finally, we also evaluated the proposed method in the online context, where the statistics of bandwidth is updated periodically. Besides, we compared the performance prediction results with measurement ones in in both offline and online contexts.