En 2010, Netflix se met à offrir ses services au Canada. L’arrivée de ce nouveau joueur vient bousculer les structures de la filière cinéma au pays en créant un décalage entre un système reposant sur une régulation et des fonds publics, et une compagnie privée qui opère sur Internet, un nouveau marché alors caractérisé par une faible réglementation. Ce fossé s’accentue d’ailleurs au fil des ans, à mesure que l’entreprise gagne en popularité au Canada et face à la relative inaction des gouvernements fédéraux qui se succèdent. Au Québec, on peut constater la grande valeur culturelle qui est accordée au cinéma québécois. Cette valeur est défendue par un discours associant la culture, et le cinéma, au rayonnement de l’identité et ses spécificités, dont celle d’être un bastion francophone en Amérique du Nord. Cette valeur identitaires ’exprime par le cinéma, mais également dans les autres sphères industrielles de la province. Afin d’expliquer ce phénomène, j’ai travaillé à partir de la question de recherche suivante: quels sont les discours publics formulés à l’égard de Netflix entre 2010 et 2017, et en quoi viennent-ils réaffirmer ou redéfinir les liens d’interdépendance entre cinéma national et industrie culturelle au Québec? Par cette question, l’objectif est de développer une compréhension des enjeux associés à l’entreprise américaine depuis son arrivée au Canada en analysant des articles de presse québécois parus entre 2010-2017 et des entrevues semi-dirigées avec des professionnels du cinéma québécois. En cohérence avec l’objet de recherche et les corpus retenus, j’ai opté pour une démarche à la fois inductive et déductive afin d’analyser les discours tels qu’ils existent en dehors de la recherche. Aux termes de l’analyse, nous verrons que Netflix s’inscrit dans une logique de concurrence à la fois industrielle mais aussi culturelle. Pour mieux comprendre ces enjeux, les principales théories qui ont été mobilisées sont celles des industries culturelles et du cinéma national.

Today's popular over-the-top (OTT) video streaming services such as YouTube, Netflix and Hulu deliver video contents to viewers using adaptive bitrate (ABR) technologies. In ABR streaming, a video player running on a viewer's device adaptively changes bitrates to match given network conditions. However, providing reliable streaming is challenging. First, an ABR player may select an inappropriate bitrate during playback due to the lack of direct knowledge of access networks, frequent user mobility and rapidly changing channel conditions. Second, OTT content is delivered to viewers without any cooperation with Internet service providers (ISPs). Last, there are no appropriate tools that evaluate the performance of ABR streaming along with video quality of experience (QoE). This thesis describes how to improve the video QoE of OTT video streaming services using ABR technologies. Our analysis starts from understanding ABR heuristics. How does ABR streaming work? What factors does an ABR player consider when switching bitrates during a download? Then, we propose our solutions to improve existing ABR streaming from the perspective of network operators who deliver video content through their networks and video service providers who build ABR players running on viewers' devices. From the network operators' point of view, we propose to find a better video content server based on round trip times (RTTs) between an edge node of a wireless network and available video content servers when a viewer requests a video. The edge node can be an Internet Service Provider (ISP) router in a Wi-Fi network and a packet data network gateway (P-GW) in a 4G network. During the experiments, our solution showed better TCP performance (e.g., higher TCP throughput during playback) 146 times out of 200 experiments (73%) over Wi-Fi networks and 162 times out of 200 experiments (81%) over 3G networks. In addition, we claim that the wireless edge nodes can assist an ABR video player in selecting the best available bitrate by controlling the available bandwidth in the radio access network between a base station and a viewer's device. In our Wi-Fi testbed, the proposed solution saved up to 21% of radio bandwidth on mobile devices and enhanced the viewing experience by reducing rebufferings during playback. Last, we assert that software-defined networking (SDN) can improve video QoE by dynamically controlling routing paths of video streaming flows based on the provisioned networking information collected from SDN-enabled networking devices. Using an off-the-shelf SDN platform, we showed that our proposed solution can reduce rebufferings by 50% and provide higher bitrates during a download. From the perspective of video service providers, higher video QoE can be achieved by improving ABR heuristics implemented in an ABR player. To support this idea, we investigated the role of playout buffer size in ABR streaming and its impact on video QoE. Through our video QoE survey, we proved that a large buffer does not always outperform a small buffer, especially under rapidly varying network conditions. Based on this finding, we suggest to dynamically change the maximum buffer size in an ABR player depending on the current capacity of its playout buffer for improving the QoE of viewers. During the experiments, our proposed solution improved the viewing experience by offering 15% higher average played bitrate, 70% fewer bitrate changes and 50% shorter rebuffering duration. Our experimental results show that even small changes of ABR heuristics and new features of network systems can greatly affect video QoE. However, it is still difficult for video service providers or network operators to evaluate new ABR heuristics or network system changes due to lack of accurate QoE monitoring systems. In order to solve this issue, we have developed YouSlow ("YouTube Too Slow!? - YouSlow") as a new approach to monitoring video QoE for the analysis of ABR performance. The lightweight web browser plug-in and mobile application are designed to monitor various playback events (e.g., rebuffering duration and frequency of bitrate changes) directly from within ABR video players and calculate statistics along with video QoE. Using YouSlow, we investigate the impact of the above playback events on video abandonment: about 10% of viewers abandoned the YouTube videos when the pre-roll ads lasted for 15 seconds. Even increasing the bitrate can annoy viewers; they prefer a high starting bitrate with no bitrate changes during playback. Our regression analysis shows that bitrate changes do not affect video abandonment significantly and the abandonment rate can be estimated accurately using the rebuffering ratio and the number of rebufferings. The thesis includes four main contributions. First, we investigate today's popular OTT video streaming services (e.g., YouTube and Netflix) that use ABR streaming technologies. Second, we propose to build QoS and QoE aware video streaming that can be implemented in existing wireless networks (e.g., Wi-Fi, 3G and 4G) and in SDN-enabled networks. Third, we propose to improve current ABR heuristics by dynamically changing the playout buffer size under varying network conditions. Last, we designed and implemented a new monitoring system for measuring video QoE.