Live multimedia streaming from mobile devices is rapidly gaining popularity but little is known about the QoE they provide. In this paper, we examine the Periscope service. We first crawl the service in order to understand its usage patterns. Then, we study the protocols used, the typical quality of experience indicators, such as playback smoothness and latency, video quality, and the energy consumption of the Android application.
We believe that this work together with the work of @cite_8 are the first to provide measurement-based analyses on the anatomy and performance of a popular mobile live streaming application. thoroughly study the delay and its origins but, similar to us, also show results on usage patterns and video stalling, particularly the impact of buffer size. They also reveal a particular vulnerability in the service.
@cite_8
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Over-the-top mobile adaptive video streaming is invariably influenced by volatile network conditions, which can cause playback interruptions (stalling or rebuffering events) and bitrate fluctuations, thereby impairing users’ quality of experience (QoE). Video quality assessment models that can accurately predict users’ QoE under such volatile network conditions are rapidly gaining attention, since these methods could enable more efficient design of quality control protocols for media-driven services such as YouTube, Amazon, Netflix, and many others. However, the development of improved QoE prediction models requires data sets of videos afflicted with diverse stalling events that have been labeled with ground-truth subjective opinion scores. Toward this end, we have created a new mobile video quality database that we call LIVE Mobile Stall Video Database-II. Our database contains a total of 174 videos afflicted with distortions caused by 26 different stalling patterns. We describe the way we simulated the diverse stalling events to create a corpus of distorted videos, and we detail the human study we conducted to obtain continuous-time subjective scores from 54 subjects. We also present the outcomes of our comprehensive analysis of the impact of several factors that influence subjective QoE, and report the performance of existing QoE-prediction models on our data set. We are making the database (videos, subjective data, and video metadata) publicly available in order to help the advance state-of-the-art research on user-centric mobile network planning and management. The database may be accessed at http: live.ece.utexas.edu research LIVEStallStudy liveMobile.html . In this paper, we analyze the YouTube service and the traffic generated from its usage. The purpose of this study is to identify by strictly using passive measurements the information that can be used as metrics or indicators of the progress of individual video sessions and to estimate the impact of these metrics in the user experience. We find a novel method to track the progress of the video playback that, in contrast to previous works, does not require instrumentation of the video player neither browser-based plug-ins. Instead, we extract important statistical information about the status of the playback by reverse engineering the metrics in related HTTP requests that are generated during playback. For the purpose of collecting these metrics, a tool was developed to perform YouTube traffic measurements by means of passive network monitoring in a large university campus network. The analysis of the obtained data revealed the most important sources of initial delay in the sessions as well as buffer outage events and download rate statistics. Further analysis revealed the impact of video advertisements and re-buffering events on the user experience in terms of video abandonment rate. We study the optimal streaming of variable bit-rate (VBR) video over a random VBR channel. The goal of a streaming application is to enable the successful decoding of each video object before its displaying deadline is violated. Hence, we define the main performance metric of a streaming system as the probability of un-interrupted video presentation, or jitter-free probability. Previous literature has described solutions to estimate the jitter-free probability by assuming either independence in the encoded data process or simplistic channel models. In this work, we present a novel analytical framework, which requires only some basic statistical information of an arbitrary VBR channel, to bound the probability of jitter-free playout under the constraint of initial playout delay and receiver buffer size. Both the infinite and finite buffer cases are considered. This technique is then applied to investigate streaming over a wireless system modeled by an extended Gilbert channel with ARQ transmission control. Experimental results with MPEG-4 VBR encoded video demonstrates that the proposed analysis derives close bounds to the actual system performance. Finally, we show that the proposed analysis provides a theoretical foundation to quantify the tradeoffs between the initial playout delay, the receiver buffer size, and the jitter-free probability for a general class of VBR streaming over random VBR channels. Stalls during video playback are perhaps the most important indicator of a client's viewing experience. To provide the best possible service, a proactive network operator may therefore want to know the buffer conditions of streaming clients and use this information to help avoid stalls due to empty buffers. However, estimation of clients' buffer conditions is complicated by most streaming services being rate-adaptive, and many of them also encrypted. Rate adaptation reduces the correlation between network throughput and client buffer conditions. Usage of HTTPS prevents operators from observing information related to video chunk requests, such as indications of rate adaptation or other HTTP-level information. This paper presents BUFFEST, a novel classification framework that can be used to classify and predict streaming clients' buffer conditions from both HTTP and HTTPS traffic. To illustrate the tradeoffs between prediction accuracy and the available information used by classifiers, we design and evaluate classifiers of different complexity. At the core of BUFFEST is an event-based buffer emulator module for detailed analysis of clients' buffer levels throughout a streaming session, as well as for automated training and evaluation of online packet-level classifiers. We then present example results using simple threshold-based classifiers and machine learning classifiers that only use TCP IP packet-level information. Our results are encouraging and show that BUFFEST can distinguish streaming clients with low buffer conditions from clients with significant buffer margin during a session even when HTTPS is used.