QoE-Based Low-Delay Live Streaming Using Throughput Predictions

Abstract

Recently, Hypertext Transfer Protocol (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 the varying network conditions to ensure a high quality of experience (QoE)—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 access network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 20 to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (short for low-latency prediction-based adaptation), which is designed to operate with a transport latency of a few seconds. To reach this goal, LOLYPOP leverages Transmission Control Protocol throughput predictions on multiple time scales, from 1 to 10 seconds, along with estimations of the relative prediction error distributions. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the QoE by maximizing the average video quality as a function of the number of skipped segments and quality transitions. 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 called FESTIVE . We observed that the average selected video representation index is by up to a factor of 3 higher than with the baseline approach. We also observed that LOLYPOP is able to reach points from a broader region in the QoE space, and thus it is better adjustable to the user profile or service provider requirements.

Bandwidth Prediction Schemes for Defining Bitrate Levels in SDN-enabled Adaptive Streaming

“…The implemented testbed was used to investigate the performance of the presented approaches under wireless network conditions, where the throughput is subject to considerable fluctuations. In order to replicate the same network conditions, we shaped the link between OVS1-OVS2 based on a real set of traces gathered from IEEE 802.11 wireless local area networks (WLANs) [24]. First Experiment: The first experiment aims to investigate the impact of the forecasting horizon and the sampling rate of the network bandwidth measurement on the accuracy of the prediction algorithm and their implications on the proposed schemes (i.e.…”

LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming

“…The metric is a weighted sum of the layer sizes for each chunk. Since the user's QoE is concave in the playback rate [8], the higher layers contribute lower to the QoE as compared to the lower layers. Thus, the weights decrease with the layer index modeling the diminishing returns for higher layers.…”

“…The choice of γ in (1) implies that all the higher layers than layer a have lower utility than a chunk at layer a for all a. User's QoE is concave in the playback rate [8], so the higher layers contribute lower to the QoE as compared to the lower layers. For example, running into base layer skips degrades the QoE much more than skipping the first enhancement layer (E1) since when E1 is skipped the video chunk is still can be played.…”

Analysis and modelling of traffic produced by adaptive HTTP-based video

“…On the other hand, we have works based on user traffic trace analysis including deep packet inspection, e.g. [8,[36][37][38]. Some works tried to investigate details of traffic characteristics, e.g.…”