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

Video traffic modeling and delivery

Video is becoming a major component of the network traffic, and thus there has been a great interest to model video traffic. It is known that video traffic possesses short range dependence (SRD) and long range dependence (LRD) properties, which can drastically affect network performance. By decomposing a video sequence into three parts, according to its motion activity, Markov-modulated self-similar process model is first proposed to capture autocorrelation function (ACF) characteristics of MPEG video traffic. Furthermore, generalized Beta distribution is proposed to model the probability density functions (PDFs) of MPEG video traffic. It is observed that the ACF of MPEG video traffic fluctuates around three envelopes, reflecting the fact that different coding methods reduce the data dependency by different amount. This observation has led to a more accurate model, structurally modulated self-similar process model, which captures the ACF of the traffic, both SRD and LRD, by exploiting the MPEG structure. This model is subsequently simplified by simply modulating three self-similar processes, resulting in a much simpler model having the same accuracy as the structurally modulated self-similar process model. To justify the validity of the proposed models for video transmission, the cell loss ratios (CLRs) of a server with a limited buffer size driven by the empirical trace are compared to those driven by the proposed models. The differences are within one order, which are hardly achievable by other models, even for the case of JPEG video traffic. In the second part of this dissertation, two dynamic bandwidth allocation algorithms are proposed for pre-recorded and real-time video delivery, respectively. One is based on scene change identification, and the other is based on frame differences. The proposed algorithms can increase the bandwidth utilization by a factor of two to five, as compared to the constant bit rate (CBR) service using peak rate assignment

Dynamic bandwidth allocation in ATM networks

Includes bibliographical references.This thesis investigates bandwidth allocation methodologies to transport new emerging bursty traffic types in ATM networks. However, existing ATM traffic management solutions are not readily able to handle the inevitable problem of congestion as result of the bursty traffic from the new emerging services. This research basically addresses bandwidth allocation issues for bursty traffic by proposing and exploring the concept of dynamic bandwidth allocation and comparing it to the traditional static bandwidth allocation schemes

Adaptation of variable-bit-rate compressed video for transport over a constant-bit-rate communication channel in broadband networks.

by Chi-yin Tse.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 118-[121]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Video Compression and Transport --- p.2Chapter 1.2 --- VBR-CBR Adaptation of Video Traffic --- p.5Chapter 1.3 --- Research Contributions --- p.7Chapter 1.3.1 --- Spatial Smoothing: Video Aggregation --- p.8Chapter 1.3.2 --- Temporal Smoothing: A Control-Theoretic Study。 --- p.8Chapter 1.4 --- Organization of Thesis --- p.9Chapter 2 --- Preliminaries --- p.13Chapter 2.1 --- MPEG Compression Scheme --- p.13Chapter 2.2 --- Problems of Transmitting MPEG Video --- p.17Chapter 2.3 --- Two-layer Coding and Transport Strategy --- p.19Chapter 2.3.1 --- Framework of MPEG-based Layering --- p.19Chapter 2.3.2 --- Transmission of GS and ES --- p.20Chapter 2.3.3 --- Problems of Two-layer Video Transmission --- p.20Chapter 3 --- Video Aggregation --- p.24Chapter 3.1 --- Motivation and Basic Concept of Video Aggregation --- p.25Chapter 3.1.1 --- Description of Video Aggregation --- p.28Chapter 3.2 --- MPEG Video Aggregation System --- p.29Chapter 3.2.1 --- Shortcomings of the MPEG Video Bundle Scenario with Two-Layer Coding and Cell-Level Multiplexing --- p.29Chapter 3.2.2 --- MPEG Video Aggregation --- p.31Chapter 3.2.3 --- MPEG Video Aggregation System Architecture --- p.33Chapter 3.3 --- Variations of MPEG Video Aggregation System --- p.35Chapter 3.4 --- Experimental Results --- p.38Chapter 3.4.1 --- Comparison of Video Aggregation and Cell-level Multi- plexing --- p.40Chapter 3.4.2 --- Varying Amount of the Allocated Bandwidth --- p.48Chapter 3.4.3 --- Varying Number of Sequences --- p.50Chapter 3.5 --- Conclusion --- p.53Chapter 3.6 --- Appendix: Alternative Implementation of MPEG Video Aggre- gation --- p.53Chapter 3.6.1 --- Profile Approach --- p.54Chapter 3.6.2 --- Bit-Plane Approach --- p.54Chapter 4 --- A Control-Theoretic Study of Video Traffic Adaptation --- p.58Chapter 4.1 --- Review of Previous Adaptation Schemes --- p.60Chapter 4.1.1 --- A Generic Model for Adaptation Scheme --- p.60Chapter 4.1.2 --- Objectives of Adaptation Controller --- p.61Chapter 4.2 --- Motivation for Control-Theoretic Study --- p.64Chapter 4.3 --- Linear Feedback Controller Model --- p.64Chapter 4.3.1 --- Encoder Model --- p.65Chapter 4.3.2 --- Adaptation Controller Model --- p.69Chapter 4.4 --- Analysis --- p.72Chapter 4.4.1 --- Stability --- p.73Chapter 4.4.2 --- Robustness against Coding-mode Switching --- p.83Chapter 4.4.3 --- Unit-Step Responses and Unit-Sample Responses --- p.84Chapter 4.5 --- Implementation --- p.91Chapter 4.6 --- Experimental Results --- p.95Chapter 4.6.1 --- Overall Performance of the Adaptation Scheme --- p.97Chapter 4.6.2 --- Weak-Control verus Strong-Control --- p.99Chapter 4.6.3 --- Varying Amount of Reserved Bandwidth --- p.101Chapter 4.7 --- Conclusion --- p.103Chapter 4.8 --- Appendix I: Further Research --- p.103Chapter 4.9 --- Appendix II: Review of Previous Adaptation Schemes --- p.106Chapter 4.9.1 --- Watanabe. et. al.'s Scheme --- p.106Chapter 4.9.2 --- MPEG's Scheme --- p.107Chapter 4.9.3 --- Lee et.al.'s Modification --- p.109Chapter 4.9.4 --- Chen's Adaptation Scheme --- p.110Chapter 5 --- Conclusion --- p.116Bibliography --- p.11