QoE-aware optimization for video delivery and storage. In: 2015 IEEE 16th International Symposium on "A World of Wireless, Mobile and MultimediaNetworks" (WoWMoM) 2015 (pp. 1-10 Abstract-The explosive growth of Over-the-top (OTT) online video strains capacity of operators' networks, which severely threatens video quality perceived by end users. Since video is very bandwidth consuming, its distribution costs are becoming too high to scale with network investments that are required to support the increasing bandwidth demand. Content providers and operators are searching for solutions to reduce this video traffic load, without degrading their customers' perceived Quality of Experience (QoE). This paper proposes a method that can programmatically optimize video content for desired QoE according to perceptual video quality and device display properties, while achieving bandwidth and storage savings for content providers, operators, and end users. The preliminary results obtained with Samsung Galaxy S3 phone show that up to 60% savings can be achieved by optimizing movies without compromising the perceptible video quality, and up to 70% for perceptible, but not annoying video quality difference. Tailoring video optimization to individual user perception can provide seamless QoE delivery across all users, with a low overhead (i.e., 10%) required to achieve this goal. Finally, two applications of video optimization: QoE-aware delivery and storage, are proposed and examined.
Abstract-This paper describes a novel QoE-aware adaptive video streaming method that enhances the viewing experience on mobile devices and reduces cellular network bandwidth consumed by Dynamic Adaptive Streaming over HTTP (DASH) by considering perceptual video quality and data rate channel conditions in the bitrate adaptation process. By streaming an optimized video for the particular video quality and channel conditions to a mobile device, we can improve the worst video qualities caused by DASH streaming and reduce quality variations using fewer number of bits.
Abstract-The arrival of smartphones and tablets, along with a flat rate mobile Internet pricing model have caused increasing adoption of mobile data services. According to recent studies, video has been the main driver of mobile data consumption, having a higher growth rate than any other mobile application. However, streaming a medium/high quality video files can be an issue in a mobile environment where available capacity needs to be shared among a large number of users. Additionally, the energy consumption in mobile devices increases proportionally with the duration of data transfers, which depend on the download data rates achievable by the device. In this respect, adoption of opportunistic content prefetching schemes that exploit times and locations with high data rates to deliver content before a user requests it, has the potential to reduce the energy consumption associated with content delivery and improve the user's quality of experience, by allowing playback of pre-stored content with virtually no perceived interruptions or delays. This paper presents a family of opportunistic content pre-fetching schemes and compares their performance to standard on-demand access to content. By adopting a simulation approach on experimental data, collected with monitoring software installed in mobile terminals, we show that content pre-fetching can reduce energy consumption of the mobile devices by up to 30% when compared to the on demand download of the same file, with a time window of 1 hour needed to complete the content prepositioning.
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