The wide adoption of multimedia service capable mobile devices, the availability of better networks with higher bandwidths, and the availability of platforms offering digital content has led to an increasing popularity of multimedia streaming services. However, multimedia streaming services can be subject to different factors that affect the quality perceived by the users, such as service interruptions or quality oscillations due to changing network conditions, particularly in mobile networks. Dynamic Adaptive Streaming over HTTP (DASH), leverages the use of content-distribution networks and the capabilities of the multimedia devices to allow multimedia players to dynamically adapt the quality of the media streaming to the available bandwidth and the device characteristics. While many elements of DASH are standardized, the algorithms providing the dynamic adaptation of the streaming are not. The adaptation is often based on the estimation of the throughput or a buffer control mechanism. In this paper, we present a new throughput estimation adaptation algorithm based on a statistical method named Adaptive Forgetting Factor (AFF). Using this method, the adaptation logic is able to react appropriately to the different conditions of different types of networks. A set of experiments with different traffic profiles show that the proposed algorithm improves video quality performance in both wired and wireless environments.

翻译：多媒体服务移动设备的广泛普及、更高带宽网络的可用性以及提供数字内容的平台的发展，使得多媒体流媒体服务日益流行。然而，多媒体流媒体服务可能受到多种影响用户感知质量的因素，例如网络条件变化（尤其是在移动网络中）导致的服务中断或质量波动。基于HTTP的动态自适应流媒体（DASH）利用内容分发网络和多媒体设备的能力，使多媒体播放器能够根据可用带宽和设备特性动态调整媒体流的质量。尽管DASH的许多元素已标准化，但提供流媒体动态自适应的算法尚未标准化。该自适应通常基于吞吐量估计或缓冲区控制机制。本文提出一种基于自适应遗忘因子（AFF）统计方法的新型吞吐量估计自适应算法。通过该方法，自适应逻辑能够针对不同类型网络的不同条件做出适当响应。针对不同流量场景的实验表明，所提算法在有线和无线环境下均能提升视频质量性能。