Abstract
Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.
Highlights
Over the past few decades, the increase in demand for video streaming has grown, driven by applications such as teleconference, Internet Protocol Television (IPTV), security systems, Video-on-Demand (VoD), and live video streaming [1]
Proportional Integral Controller Enhanced (PIE) may randomly drop a packet in the presence of congestion; congestion detection is based on the queuing latency like Controlled Delay (CoDel) instead of the queue length like conventional Active Queue Management (AQM) schemes
We propose an AQM strategy that randomly discards packets according to a predicted queue delay, as CoDel and PIE
Summary
Over the past few decades, the increase in demand for video streaming has grown, driven by applications such as teleconference, Internet Protocol Television (IPTV), security systems, Video-on-Demand (VoD), and live video streaming [1]. As an alternative to reduce packet losses, the Internet Service Providers (ISPs) increase the router’s buffer length in an attempt to better accommodate the traffic This trend was driven by cheaper memory prices [7], which in turn may dramatically increase end-to-end latency and jitter, and severely impairing the perceived quality of live video streaming. Based on the forecast of queue delay, the new proposed method performs a random early discard to prevent future congestion, forcing traffic sources to reduce their transmission rate. This process induces the DASH clients to decrease the quality.
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