VLQoE: Video QoE instrumentation on the smartphone

Abstract

The usage of network-demanding applications is growing rapidly such as video streaming on mobile terminals. However, network and/or service providers might not guarantee the perceived quality for video streaming that demands high packet transmission rate. In order to satisfy the user expectations and to minimize user churn, it is important for network operators to infer the end-user perceived quality in video streaming. Today, the most reliable method to obtain end-user perceived quality is through subjective tests, and the preferred location is the user interface as it is the closest point of application to the enduser. The end-user perceived quality on video streaming is highly influenced by occasional freezes; technically the extraordinary time gaps between two consecutive pictures that are displayed to the user, i.e., high inter-picture time. In this paper, we present a QoE instrumentation for video streaming, VLQoE. We added functionality to the VLC player to record Multimed Tools Appl a set of metrics from the user interface, application-level, network-level, and from the available sensors of the device. To the best of our knowledge, VLQoE is the first tool of its kind that can be used in user experiments for video streaming. By using the tool, we present a two state model based on the inter-picture time, for the HTTP-and RTSP-based video streaming via 3.5G. Next, we studied the influence of inter-picture time on the user perceived quality through out a user study. We investigated the minimum user perceived inter-picture time, and the user response time.

Impact of packet loss on the perceived quality of UDP-based multimedia streaming: a study of user quality of experience in real-life environments

“…In contrast, the distribution of the quality distortions can be modeled with, for instance, two-state exponential model as it is done in [17]. The model assumes that a video stream can be in one of the two states (ON or OFF).…”

“…In the ON state, the stream is unaffected by the quality degradations, while in the OFF state, the quality is degraded. In [17], the authors investigate the inter-picture time in cellular-based video stream and define that if the time is ≤100 ms then the stream is in the ON state. Otherwise, the stream enters the OFF state.…”

“…In [17], Ickin et al continued their previous work presented in [8] and developed a QoE evaluation methodology for Android-based smartphones. The methodology was used for user QoE analysis for mobile video streaming.…”

Real-Time QoE Monitoring System for Video Streaming Services with Adaptive Media Playout

“…Since the QoE evaluation for streaming services becomes more and more important, several QoE evaluation or monitoring tools were developed [15][16][17][18][19]. A QoE monitoring tool for multimedia quality assessment in NS-3 network simulator was presented in [15].…”

“…In [18], the proposed concept, Monitoring of Audio Visual Quality by Key Performance Indicators (MOAVI), is able to isolate and focus investigation, set up algorithms, increase the monitoring period, and guarantee better prediction of perceptual quality. In [19], Ickin et al added functionality to the VideoLAN Client (VLC) media player [20] to record a set of metrics from the user interface, application-level, network-level, and the available sensors of the device. Based on these metrics, the proposed VLQoE, a QoE instrumentation for video streaming, can infer the QoE of streaming services on a smartphone.…”