Accurate cross-layer information is very useful to optimize mobile networks for specific applications. However, providing application-layer information to lower protocol layers has become very difficult due to the wide adoption of end-to-end encryption and due to the absence of cross-layer signaling standards. As an alternative, this paper presents a traffic profiling solution to passively estimate parameters of HTTP Adaptive Streaming (HAS) applications at the lower layers. By observing IP packet arrivals, our machine learning system identifies video flows and detects the state of an HAS client's play-back buffer in real time. Our experiments with YouTube's mobile client show that Random Forests achieve very high accuracy even with a strong variation of link quality. Since this high performance is achieved at IP level with a small, generic feature set, our approach requires no Deep Packet Inspection (DPI), comes at low complexity, and does not interfere with end-to-end encryption. Traffic profiling is, thus, a powerful new tool for monitoring and managing even encrypted HAS traffic in mobile networks.
When it comes to video streaming, most of the recent works propose to distinguish the entire video session into different classes. For example, the authors in @cite_4 propose a system that monitors application-level quality indicators and corresponding traffic traces to classify YouTube videos into three QoE classes. Similarly, different levels of QoE are studied in @cite_22 with the focus on stalling, average video quality and quality variations as the key influence factors. A causal analysis between QoE and Quality of Service (QoS) is presented in @cite_27 with features from application and network-layer QoS metrics, while an approach to discriminate between audio and video HAS flows is proposed in @cite_12 . Compared to these studies that classify an entire video session into a single category, our classification is performed at a higher temporal resolution by estimating dynamic video traffic parameters in real time.
@cite_27 @cite_4 @cite_22 @cite_12
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The relationship between the user perceived Quality of Experience (QoE) with Internet applications and the Quality of Service (QoS) of the underlying network and applications is complex. Unveiling statistical relations between QoE and QoS can boost the prediction and diagnosis of QoE. In this paper, we shed light on the relationship between QoE and QoS for a popular application: YouTube video streaming. We conducted a controlled study where we asked users to rate their perceived quality of YouTube videos under different network conditions. During this experiments, we also captured network QoS and application QoS. We then analyze the resulting dataset with SES, a feature selection algorithm that identifies multiple, minimal-size, statistically-equivalent signatures with maximal predictive power for a target variable (e.g., QoE). We found that we can build optimal QoE predictors using a minimal signature of only three features from application or network QoS metrics compared to four when we consider features from both layers. Due to the widespread use of encryption in Over-The-Top video streaming traffic, network operators generally lack insight into application-level quality indicators (e.g., video quality levels, buffer underruns, stalling duration). They are thus faced with the challenge of finding solutions for monitoring service performance and estimating customer Quality of Experience (QoE) degradations based solely on passive monitoring solutions deployed within their network. We address this challenge by considering the concrete case of YouTube, whereby we present a methodology for the classification of end users' QoE when watching YouTube videos, based only on statistical properties of encrypted network traffic. We have developed a system called YouQ which includes tools for monitoring and analysis of application-level quality indicators and corresponding traffic traces. Collected data is then used for the development of machine learning models for QoE classification based on computed traffic features per video session. To test the YouQ system and methodology, we collected a dataset corresponding to 1060 different YouTube videos streamed across 39 different bandwidth scenarios, and tested various classification models. Classification accuracy was found to be up to 84 when using three QoE classes ("low", "medium" or "high") and up to 91 when using binary classification (classes "low" and "high"). To improve the models in the future, we discuss why and when prediction errors occur. Moreover, we have analysed YouTube's adaptation algorithm, thus providing valuable insight into the logic behind the quality level selection strategy, which may also be of interest in improving future QoE estimation algorithms. Tracking and maintaining satisfactory QoE for video streaming services is becoming a greater challenge for mobile network operators than ever before. Downloading and watching video content on mobile devices is currently a growing trend among users, that is causing a demand for higher bandwidth and better provisioning throughout the network infrastructure. At the same time, popular demand for privacy has led many online streaming services to adopt end-to-end encryption, leaving providers with only a handful of indicators for identifying QoE issues. In order to address these challenges, we propose a novel methodology for detecting video streaming QoE issues from encrypted traffic. We develop predictive models for detecting different levels of QoE degradation that is caused by three key influence factors, i.e. stalling, the average video quality and the quality variations. The models are then evaluated on the production network of a large scale mobile operator, where we show that despite encryption our methodology is able to accurately detect QoE problems with 72 -92 accuracy, while even higher performance is achieved when dealing with cleartext traffic Streaming media applications generate a sizable part of network traffic and represent a significant proportion of network providers' income. The commitment to user satisfaction can be summarized by different concepts, content providers emphasizing quality of experience (QoE), whereas network providers are more focused on quality of service (QoS). Measuring QoS parameters, and understanding the relationship between the two, is essential to enable network tuning for enhanced QoE. Analysis of actual streaming dynamics and detection of impairments require the ability to discriminate between audio and video packet flows. For the purpose we present a cross-layer analysis based on the study of packet flow features obtained by implementing a Support Vector Machine. This paper presents results of a study supported by the use of nTh HighSee, a non-intrusive QoS monitoring tool, where approaches to video audio detection have been investigated and tested.