On the Identification and Prediction of Stalling Events to Improve QoE in Video Streaming

J.-M Martinez-Caro,M.-D Cano

doi:10.3390/electronics10060753

Abstract

Monitoring the Quality of user Experience is a challenge for video streaming services. Models for Quality of User Experience (QoE) evaluation such as the ITU-T Rec. P.1203 are very promising. Among the input data that they require are the occurrence and duration of stalling events. A stalling even5 is an interruption in the playback of multimedia content, and its negative impact on QoE is immense. Given the idiosyncrasy of this type of event, to count it and its duration is a complex task to be automated, i.e., without the participation of the user who visualizes the events or without direct access to the final device. In this work, we propose two methods to overcome these limitations in video streaming using the DASH framework. The first method is intended to detect stalling events. For simplicity, it is based on the behavior of the transport layer data and is able to classify an IP packet as belonging (or not) to a stalling event. The second method aims to predict if the next IP packet of a multimedia stream will belong to a stalling event (or not), using a recurrent neural network with a variant of the Long Short–Term Memory (LSTM). Our results show that the detection model is able to spot the occurrence of a stalling event before being experienced by the user, and the prediction model is able to forecast if the next packet will belong to a stalling event with an error rate of 10.83%, achieving an F1 score of 0.923.

Highlights

Video streaming services have become very popular
From our point of view, can be dow Forthe small values of t, overfitting occurred duethis to insufficient seen an advantage, since proactive could be applied in service/system in data.asThis effect was clearly avoidedactions by increasing t because thethe amount of false positives order to avoid the stalling event before it is experienced by the final user
In terms of quality monitoring, numerous Quality of User Experience (QoE) models have been proposed in the related literature, and one common factor among all of them is the number and duration of stalling events

Summary

Introduction

In 2020, the revenue of video streaming platforms amounted to US$25,894 million, and the number of users is expected to amount to 1.3 billion by 2024 [1]. As a matter of fact, video streaming platforms are showing that “dynamic markets can benefit consumers with lower prices and better quality” [2]. When it comes to evaluating quality, two concepts arise, Quality of Service (QoS) and Quality of user Experience (QoE). The former is mainly based on well-known network metrics (delay, jitter, etc.). Most multimedia services consider visual quality, loading time, stalling events, and overall quality as the four basic metrics for evaluating the quality of the playback [3]

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