QoE-Based Low-Delay Live Streaming Using Throughput Predictions

Recently, HTTP-based adaptive streaming has become the de facto standard for video streaming over the Internet. It allows clients to dynamically adapt media characteristics to network conditions in order to ensure a high quality of experience, that is, minimize playback interruptions, while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (Low-Latency Prediction-Based Adaptation) that is designed to operate with a transport latency of few seconds. To reach this goal, LOLYPOP leverages TCP throughput predictions on multiple time scales, from 1 to 10 seconds, along with an estimate of the prediction error distribution. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the quality of experience by maximizing the average video quality as a function of the number of skipped segments and quality transitions. In order to select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm from the literature, called FESTIVE. We observed that the average video quality is by up to a factor of 3 higher than with FESTIVE. We also observed that LOLYPOP is able to reach a broader region in the quality of experience space, and thus it is better adjustable to the user profile or service provider requirements.Comment: Technical Report TKN-16-001, Telecommunication Networks Group, Technische Universitaet Berlin. This TR updated TR TKN-15-00

Quality driven resource allocation for adaptive video streaming in OFDMA uplink

Abstract—In this paper, we consider the problem of improv-ing Quality of Experience (QoE) of multiple video streams in OFDMA uplink. The proposed system leverages both video adaptation and adaptation of resource allocation (RA). Three different time scales arises in this scenario, namely (1) long time scale of QoE, evaluated over the sequence of Groups of Pictures (GOPs), (2) medium time scale of video adaptation for each GOP, and (3) short time scale of RA inside each GOP. We deal with the time scale differences through a two step process. We first formulate, for each GOP, an optimization problem of video quality which takes into account the long time scale QoE. Then we convert this problem to a sequence of quality-driven RA optimization. At each RA step inside a GOP, resources are allocated to users according to their utility determined by QoE constraints and quality fairness. The performance of proposed quality-driven RA is evaluated under realistic uplink scenario. It is shown that the proposed system improves significantly users’ QoE compared to other solutions. I