Transmisión inalámbrica multimedia coordinada con DASH-SAND

[EN] The adoption of DASH (Dynamic Adaptive Streaming over HTTP) as a video transmission standard has introduced new challenges in environments with high density of Wi-Fi clients. If video players do not take into account the playback status of other devices in the network unfair bandwidth distribution may cause playback problems (stalls, low playback quality, etc.). This paper proposes a collaborative playback technique based on DASH-SAND (MPEG¿s Server and Network Assisted DASH) that aims to reduce, in a collaborative way, the number of interruptions in the playback of multimedia content in Wi-Fi environments with high density of users. Results prove that the proposed solution reduces both the number and duration of stalls at the expense of decreasing the average representation of the playback.Este trabajo ha sido subvencionado por el Programa PAID10-18 así como por el Proyecto Línea de I+D+i Tecnologías de distribución y procesado de información multimedia y QoE de la Universitat Politècnica de València.Belda Ortega, R.; De Fez Lava, I.; Guerri Cebollada, JC. (2020). Transmisión inalámbrica multimedia coordinada con DASH-SAND. Universidad de Málaga. 1-4. http://hdl.handle.net/10251/178577S1

Price-Based Controller for Utility-Aware HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) permits to efficiently deliver video to multiple heterogenous users in a fully distributed way. This might however lead to unfair bandwidth utilization among HAS users. Therefore, network-assisted HAS systems have been proposed where network elements operate alongside with the clients adaptation logic for improving users satisfaction. However, current solutions rely on the assumption that network elements have full knowledge of the network status, which is not always realistic. In this work, we rather propose a practical network-assisted HAS system where the network elements infer the network link congestion using measurements collected from the client endpoints, the congestion level signal is then used by the clients to optimize their video data requests. Our novel controller maximizes the overall users satisfaction and the clients share the available bandwidth fairly from a utility perspective, as demonstrated by simulation results obtained on a network simulator

Improving mobile video quality through predictive channel quality based buffering

Frequent variations in throughput make mobile networks a challenging environment for video streaming. Current video players deal with those variations by matching video quality to network throughput. However, this adaptation strategy results in frequent changes of video resolution and bitrate, which negatively impacts the users' streaming experience. Alternatively, keeping the video quality constant would improve the experience, but puts additional demand on the network. Downloading high quality content when channel quality is low requires additional resources, because data transfer efficiency is linked to channel quality. In this paper, we present a predictive Channel Quality based Buffering Strategy (CQBS) that lets the video buffer grow when channel quality is good, and relies on this buffer when channel quality decreases. Our strategy is the outcome of a Markov Decision Process. The underlying Markov chain is conditioned on 377 real-world LTE channel quality traces that we have collected using an Android mobile application. With our strategy, mobile network providers can deliver constant quality video streams, using less network resources

A machine learning-based framework for preventing video freezes in HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) represents the dominant technology to deliver videos over the Internet, due to its ability to adapt the video quality to the available bandwidth. Despite that, HAS clients can still suffer from freezes in the video playout, the main factor influencing users' Quality of Experience (QoE). To reduce video freezes, we propose a network-based framework, where a network controller prioritizes the delivery of particular video segments to prevent freezes at the clients. This framework is based on OpenFlow, a widely adopted protocol to implement the software-defined networking principle. The main element of the controller is a Machine Learning (ML) engine based on the random undersampling boosting algorithm and fuzzy logic, which can detect when a client is close to a freeze and drive the network prioritization to avoid it. This decision is based on measurements collected from the network nodes only, without any knowledge on the streamed videos or on the clients' characteristics. In this paper, we detail the design of the proposed ML-based framework and compare its performance with other benchmarking HAS solutions, under various video streaming scenarios. Particularly, we show through extensive experimentation that the proposed approach can reduce video freezes and freeze time with about 65% and 45% respectively, when compared to benchmarking algorithms. These results represent a major improvement for the QoE of the users watching multimedia content online

Price-based Controller for Quality-Fair HTTP Adaptive Streaming

HTTP adaptive streaming (HAS) has become the universal technology for video streaming over the Internet. Many HAS system designs aim at sharing the network bandwidth in a rate-fair manner. However, rate fairness is in general not equivalent to quality fairness as different video sequences might have different characteristics and resource requirements. In this work, we focus on this limitation and propose a novel controller for HAS clients that is able to reach quality fairness while preserving the main characteristics of HAS systems and with a limited support from the network devices. In particular, we adopt a price-based mechanism in order to build a controller that maximizes the aggregate video quality for a set of HAS clients that share a common bottleneck. When network resources are scarce, the clients with simple video sequences reduce the requested bitrate in favor of users that subscribe to more complex video sequences, leading to a more efficient network usage. The proposed controller has been implemented in a network simulator, and the simulation results demonstrate its ability to share the available bandwidth among the HAS users in a quality-fair manner

Quality of experience-centric management of adaptive video streaming services : status and challenges

Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming ( HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users' QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users' QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users' QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server-and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years

Edge computing assisted adaptive mobile video streaming

Nearly all bitrate adaptive video content delivered today is streamed using protocols that run a purely client based adaptation logic. The resulting lack of coordination may lead to suboptimal user experience and resource utilization. As a response, approaches that include the network and servers in the adaptation process are emerging. In this article, we present an optimized solution for network assisted adaptation specifically targeted to mobile streaming in multi-access edge computing (MEC) environments. Due to NP-Hardness of the problem, we have designed a heuristic-based algorithm with minimum need for parameter tuning and having relatively low complexity. We then study the performance of this solution against two popular client-based solutions, namely Buffer-Based Adaptation (BBA) and Rate-Based Adaptation (RBA), as well as to another network assisted solution. Our objective is two fold: First, we want to demonstrate the efficiency of our solution and second to quantify the benefits of network-assisted adaptation over the client-based approaches in mobile edge computing scenarios. The results from our simulations reveal that the network assisted adaptation clearly outperforms the purely client-based DASH heuristics in some of the metrics, not all of them, particularly, in situations when the achievable throughput is moderately high or the link quality of the mobile clients does not differ from each other substantially

A policy-based framework towards smooth adaptive playback for dynamic video streaming over HTTP

The growth of video streaming in the Internet in the last few years has been highly significant and promises to continue in the future. This fact is related to the growth of Internet users and especially with the diversification of the end-user devices that happens nowadays. Earlier video streaming solutions didn´t consider adequately the Quality of Experience from the user’s perspective. This weakness has been since overcame with the DASH video streaming. The main feature of this protocol is to provide different versions, in terms of quality, of the same content. This way, depending on the status of the network infrastructure between the video server and the user device, the DASH protocol automatically selects the more adequate content version. Thus, it provides to the user the best possible quality for the consumption of that content. The main issue with the DASH protocol is associated to the loop, between each client and video server, which controls the rate of the video stream. In fact, as the network congestion increases, the client requests to the server a video stream with a lower rate. Nevertheless, due to the network latency, the DASH protocol in a standalone way may not be able to stabilize the video stream rate at a level that can guarantee a satisfactory QoE to the end-users. Network programming is a very active and popular topic in the field of network infrastructures management. In this area, the Software Defined Networking paradigm is an approach where a network controller, with a relatively abstracted view of the physical network infrastructure, tries to perform a more efficient management of the data path. The current work studies the combination of the DASH protocol and the Software Defined Networking paradigm in order to achieve a more adequate sharing of the network resources that could benefit both the users’ QoE and network management.O streaming de vídeo na Internet é um fenómeno que tem vindo a crescer de forma significativa nos últimos anos e que promete continuar a crescer no futuro. Este facto está associado ao aumento do número de utilizadores na Internet e, sobretudo, à crescente diversificação de dispositivos que se verifica atualmente. As primeiras soluções utilizadas no streaming de vídeo não acomodavam adequadamente o ponto de vista do utilizador na avaliação da qualidade do vídeo, i.e., a Qualidade de Experiência (QoE) do utilizador. Esta debilidade foi suplantada com o protocolo de streaming de vídeo adaptativo DASH. A principal funcionalidade deste protocolo é fornecer diferente versões, em termos de qualidade, para o mesmo conteúdo. Desta forma, dependendo do estado da infraestrutura de rede entre o servidor de vídeo e o dispositivo do utilizador, o protocolo DASH seleciona automaticamente a versão do conteúdo mais adequada a essas condições. Tal permite fornecer ao utilizador a melhor qualidade possível para o consumo deste conteúdo. O principal problema com o protocolo DASH está associado com o ciclo, entre cada cliente e o servidor de vídeo, que controla o débito de cada fluxo de vídeo. De facto, à medida que a rede fica congestionada, o cliente irá começar a requerer ao servidor um fluxo de vídeo com um débito menor. Ainda assim, devido à latência da rede, o protocolo DASH pode não ser capaz por si só de estabilizar o débito do fluxo de vídeo num nível que consiga garantir uma QoE satisfatória para os utilizadores. A programação de redes é uma área muito popular e ativa na gestão de infraestruturas de redes. Nesta área, o paradigma de Software Defined Networking é uma abordagem onde um controlador da rede, com um ponto de vista relativamente abstrato da infraestrutura física da rede, tenta desempenhar uma gestão mais eficiente do encaminhamento de rede. Neste trabalho estuda-se a junção do protocolo DASH e do paradigma de Software Defined Networking, de forma a atingir uma partilha mais adequada dos recursos da rede. O objetivo é implementar uma solução que seja benéfica tanto para a qualidade de experiência dos utilizadores como para a gestão da rede