Optimized Adaptive Streaming Representations based on System Dynamics

Adaptive streaming addresses the increasing and heterogenous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) has a different resolution and bit rate, aimed at a specific set of users, like TV or mobile phone clients. While most existing works on adaptive streaming deal with effective playout-control strategies at the client side, we take in this paper a providers' perspective and propose solutions to improve user satisfaction by optimizing the encoding rates of the video sequences. We formulate an integer linear program that maximizes users' average satisfaction, taking into account the network dynamics, the video content information, and the user population characteristics. The solution of the optimization is a set of encoding parameters that permit to create different streams to robustly satisfy users' requests over time. We simulate multiple adaptive streaming sessions characterized by realistic network connections models, where the proposed solution outperforms commonly used vendor recommendations, in terms of user satisfaction but also in terms of fairness and outage probability. The simulation results further show that video content information as well as network constraints and users' statistics play a crucial role in selecting proper encoding parameters to provide fairness a mong users and to reduce network resource usage. We finally propose a few practical guidelines that can be used to choose the encoding parameters based on the user base characteristics, the network capacity and the type of video content

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

Network-based dynamic prioritization of HTTP adaptive streams to avoid video freezes

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services over the Internet. In HAS, each video is segmented and stored in different qualities. Rate adaptation heuristics, deployed at the client, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Current heuristics under-perform when sudden bandwidth drops occur, therefore leading to freezes in the video play-out, the main factor influencing users' Quality of Experience (QoE). In this article, we propose an Openflow-based framework capable of increasing clients' QoE by reducing video freezes. An Openflow-controller is in charge of introducing prioritized delivery of HAS segments, based on feedback collected from both the network nodes and the clients. To reduce the side-effects introduced by prioritization on the bandwidth estimation of the clients, we introduce a novel mechanism to inform the clients about the prioritization status of the downloaded segments without introducing overhead into the network. This information is then used to correct the estimated bandwidth in case of prioritized delivery. By evaluating this novel approach through emulation, under varying network conditions and in several multi-client scenarios, we show how the proposed approach can reduce freezes up to 75% compared to state-of-the-art heuristics

A study of learning experience with a dash-based multimedia delivery system

In order to create an improved learning experience in variable network delivery conditions, multimedia content adjustment is performed when delivered over existing network environments. This paper introduces a study of user learning when multimedia-based study material is distributed at different quality levels in the context of the European Horizon2020 project NEWTON. This paper studies the learning experience with multimedia when employing an MPEG-DASH-based adaptive multimedia delivery in a real life subjective experiment with 88 Data Network students from two Irish and Slovak universities

QoE-driven rate adaptation heuristic for fair adaptive video streaming

HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for video streaming services. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate level to be dynamically requested, based on the current network conditions. It has been shown that today's heuristics underperform when multiple clients consume video at the same time, due to fairness issues among clients. Concretely, this means that different clients negatively influence each other as they compete for shared network resources. In this article, we propose a novel rate adaptation algorithm called FINEAS (Fair In-Network Enhanced Adaptive Streaming), capable of increasing clients' Quality of Experience (QoE) and achieving fairness in a multiclient setting. A key element of this approach is an in-network system of coordination proxies in charge of facilitating fair resource sharing among clients. The strength of this approach is threefold. First, fairness is achieved without explicit communication among clients and thus no significant overhead is introduced into the network. Second, the system of coordination proxies is transparent to the clients, that is, the clients do not need to be aware of its presence. Third, the HAS principle is maintained, as the in-network components only provide the clients with new information and suggestions, while the rate adaptation decision remains the sole responsibility of the clients themselves. We evaluate this novel approach through simulations, under highly variable bandwidth conditions and in several multiclient scenarios. We show how the proposed approach can improve fairness up to 80% compared to state-of-the-art HAS heuristics in a scenario with three networks, each containing 30 clients streaming video at the same time

A quality of experience approach in smartphone video selection framework for energy efficiency

Online video streaming is getting more common in the smartphone device nowadays. Since the Corona Virus (COVID-19) pandemic hit all human across the globe in 2020, the usage of online streaming among smartphone user are getting more vital. Nevertheless, video streaming can cause the smartphone energy to drain quickly without user to realize it. Also, saving energy alone is not the most significant issues especially if with the lack of attention on the user Quality of Experience (QoE). A smartphones energy management is crucial to overcome both of these issues. Thus, a QoE Mobile Video Selection (QMVS) framework is proposed. The QMVS framework will govern the tradeoff between energy efficiency and user QoE in the smartphone device. In QMVS, video streaming will be using Dynamic Video Attribute Pre-Scheduling (DVAP) algorithm to determine the energy efficiency in smartphone devices. This process manages the video attribute such as brightness, resolution, and frame rate by turning to Video Content Selection (VCS). DVAP is handling a set of rule in the Rule Post-Pruning (RPP) method to remove an unused node in list tree of VCS. Next, QoE subjective method is used to obtain the Mean Opinion Score (MOS) of users from a survey experiment on QoE. After both experiment results (MOS and energy) are established, the linear regression technique is used to find the relationship between energy consumption and user QoE (MOS). The last process is to analyze the relationship of VCS results by comparing the DVAP to other recent video streaming applications available. Summary of experimental results demonstrate the significant reduction of 10% to 20% energy consumption along with considerable acceptance of user QoE. The VCS outcomes are essential to help users and developer deciding which suitable video streaming format that can satisfy energy consumption and user QoE