Analysis of YouTube user experience from passive measurements

Abstract

Abstract-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.

Network side lightweight and scalable YouTube QoE estimation

“…These parameters are not officially documented by YouTube but can be intuitively detected by observing the correlation between their value and the visual stallings in the video. The "rt" parameter was already identified [10] and it indicates the amount of total time (including stallings) elapsed since the start of the playback. The "et" may be a relatively new parameter not disclosed in the prior art.…”

“…These pieces of information are extracted from HTTP header fields and URL parameters [10], such as the "len" for the duration and "clen" for the number of bytes. The MS continuously measured the amount of data downloaded from the total video (which may be less than the total size in case the session was not completed), the corresponding download time, and the , and activity factor metrics introduced in this paper.…”

“…First, an explicit buffer model [7] was implemented to collect precise stalling information for the videos (note that this model was used only for the validation of the metrics introduced in this paper and not for the statistical evaluation of all sessions). Second, the quality reports of the YouTube player itself were captured from the network side based on the "s" reports [10]. The player feedback is conveyed as HTTP requests sent by the UE in every 20-30 seconds to the YouTube server while a video session is active.…”

“…1. Each chunk is requested by the player via dedicated HTTP messages, using YouTube specific URL parameters [10] or more recently the standard HTTP Range [11] request field to indicate the requested byte range.…”

A Window-Based, Server-Assisted P2P Network for VoD Services with QoE Guarantees

“…In order to evaluate the performance of the proposed system, we are considering some currently implemented features of YouTube service by using the measurements reported by [26,27]. According to [26], the download strategies that are used to download a video in YouTube depend on the network and the device that are involved.…”