DASH Adaptation Algorithm Based on Adaptive Forgetting Factor Estimation

TL;DR

The paper tackles DASH adaptation in networks with fluctuating bandwidth by introducing Adaptive Forgetting Factor (AFF) for throughput estimation. AFF updates a dynamic forgetting factor to weight recent throughput measurements more heavily, enabling rapid response to short-term variations and improving video quality across wired and wireless environments. Comparisons against avg-last-3 and EWMA show AFF can reduce stalls and bitrate switches, offering competitive QoE while maintaining fair bandwidth sharing among multiple clients. The work demonstrates a practical, parameter-light approach that can be integrated into existing DASH clients to enhance robustness in diverse network conditions.

Abstract

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.

Figures (9)

• Figure 1: Block diagram of the adaptation logic of dash.js.
• Figure 2: Experiment scenario.
• Figure 3: Bandwidth profiles used.
• Figure 4: Estimated throughput of the methods and lambda in bandwidth profile Test 1.
• Figure 5: (a) CDF of bitrate and (b) CDF of buffer level in Test 1.