Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach

HTTP based adaptive video streaming has become a popular choice of streaming due to the reliable transmission and the flexibility offered to adapt to varying network conditions. However, due to rate adaptation in adaptive streaming, the quality of the videos at the client keeps varying with time depending on the end-to-end network conditions. Further, varying network conditions can lead to the video client running out of playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). It is important to quantify the perceptual QoE of the streaming video users and monitor the same in a continuous manner so that the QoE degradation can be minimized. However, the continuous evaluation of QoE is challenging as it is determined by complex dynamic interactions among the QoE influencing factors. Towards this end, we present LSTM-QoE, a recurrent neural network based QoE prediction model using a Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded LSTM blocks to capture the nonlinearities and the complex temporal dependencies involved in the time varying QoE. Based on an evaluation over several publicly available continuous QoE databases, we demonstrate that the LSTM-QoE has the capability to model the QoE dynamics effectively. We compare the proposed model with the state-of-the-art QoE prediction models and show that it provides superior performance across these databases. Further, we discuss the state space perspective for the LSTM-QoE and show the efficacy of the state space modeling approaches for QoE prediction

QoE in Pull Based P2P-TV Systems: Overlay Topology Design Tradeoff

Abstract—This paper presents a systematic performance anal-ysis of pull P2P video streaming systems for live applications, providing guidelines for the design of the overlay topology and the chunk scheduling algorithm. The contribution of the paper is threefold: 1) we propose a realistic simulative model of the system that represents the effects of access bandwidth heterogeneity, latencies, peculiar characteristics of the video, while still guaranteeing good scalability properties; 2) we propose a new latency/bandwidth-aware overlay topology design strategy that improves application layer performance while reducing the underlying transport network stress; 3) we investigate the impact of chunk scheduling algorithms that explicitly exploit properties of encoded video. Results show that our proposal jointly improves the actual Quality of Experience of users and reduces the cost the transport network has to support. I

Flow Level QoE of Video Streaming in Wireless Networks

The Quality of Experience (QoE) of streaming service is often degraded by frequent playback interruptions. To mitigate the interruptions, the media player prefetches streaming contents before starting playback, at a cost of delay. We study the QoE of streaming from the perspective of flow dynamics. First, a framework is developed for QoE when streaming users join the network randomly and leave after downloading completion. We compute the distribution of prefetching delay using partial differential equations (PDEs), and the probability generating function of playout buffer starvations using ordinary differential equations (ODEs) for CBR streaming. Second, we extend our framework to characterize the throughput variation caused by opportunistic scheduling at the base station, and the playback variation of VBR streaming. Our study reveals that the flow dynamics is the fundamental reason of playback starvation. The QoE of streaming service is dominated by the first moments such as the average throughput of opportunistic scheduling and the mean playback rate. While the variances of throughput and playback rate have very limited impact on starvation behavior.Comment: 14 page

How to Perform AMP? Cubic Adjustments for Improving the QoE

[EN] Adaptive Media Playout (AMP) consists of smoothly and dynamically adjusting the media playout rate to recover from undesired (e.g., buffer overflow/underflow or out-of-sync) situations. The existing AMP solutions are mainly characterized by two main aspects. The first one is their goal (e.g., keeping the buffers¿ occupancy into safe ranges or enabling media synchronization). The second one is the criteria that determine the need for triggering the playout adjustments (e.g., buffer fullness or asynchrony levels). This paper instead focuses on a third key aspect, which has not been sufficiently investigated yet: the specific adjustment strategy to be performed. In particular, we propose a novel AMP strategy, called Cubic AMP, which is based on employing a cubic interpolation method to adjust a deviated playout point to a given reference. On the one hand, mathematical analysis and graphical examples show that our proposal provides superior performance than other existing linear and quadratic AMP strategies in terms of the smoothness of the playout curve, while significantly outperforming the quadratic AMP strategy regarding the duration of the adjustment period and without increasing the computational complexity. It has also been proved and discussed that higher-order polynomial interpolation methods are less convenient than cubic ones. On the other hand, the results of subjective tests confirm that our proposal provides better Quality of Experience (QoE) than the other existing AMP strategies.This work has been funded, partially, by the “Fondo Europeo de Desarrollo Regional (FEDER)” and the Spanish Ministry of Economy and Competitiveness, under its R&D&I Support Program, in project with Ref. TEC2013-45492-R.Montagud, M.; Boronat, F.; Roig, B.; Sapena Piera, A. (2017). How to Perform AMP? Cubic Adjustments for Improving the QoE. Computer Communications. 103:61-73. https://doi.org/10.1016/j.comcom.2017.01.017S617310