Using Viewing Statistics to Control Energy and Traffic Overhead in Mobile Video Streaming

Abstract

Video streaming can drain a smartphone battery quickly. A large part of the energy consumed goes to wireless communication. In this article, we first study the energy efficiency of different video content delivery strategies used by service providers and identify a number of sources of energy inefficiency. Specifically, we find a fundamental tradeoff in energy waste between prefetching small and large chunks of video content: small chunks are bad because each download causes a fixed tail energy to be spent regardless of the amount of content downloaded, whereas large chunks increase the risk of downloading data that user will never view because of abandoning the video. Hence, the key to optimal strategy lies in the ability to predict when the user might abandon viewing prematurely. We then propose an algorithm called eSchedule that uses viewing statistics to predict viewer behavior and computes an energy optimal download strategy for a given mobile client. The algorithm also includes a mechanism for explicit control of traffic overhead, i.e., unnecessary download of content that the user will never watch. Our evaluation results suggest that the algorithm can cut the energy waste down to less than half compared to other strategies. We also present and experiment with an Android prototype that integrates eSchedule into a YouTube downloader.

Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

“…We note that the origin server normally keeps the information about the clients' retention with respect to different videos which can be communicated with the edge servers. For instance, YouTube content delivery networks (CDNs) record some information about the clients' retention pattern when they watch some popular set of videos [28]. The second probability term which is in fact an estimation that the clients will request the bitrate in question according to their network conditions is computed based on their requested bitrates during the past time slots.…”

Understanding Data Usage Patterns of Geographically Diverse Mobile Users

“…The inter-session gap per user indicates the regularity of such sessions and data transferring behavior of applications. Some video streaming applications download a chunk of content every few seconds, where the length of the download depends on how much content is needed to fill the players buffer size [41]. A study on Netflix streaming by Adhikari et al [42] shows that once the player buffer has filled, the subsequent download events happen at about every four seconds interval.…”

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“…The example applications are at the bottom. delivery [28], QoE prediction and optimization [14,22], and traffic classification [24]. modern multimedia services communicate over HTTPS [26].…”