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Mobile edge caching will represent a 5G service able to improve the quality of experience of users served on mobile networks. Video streaming is the candidate traffic category which will leverage such service. In turn, the availability of rate-adaptive video playout mechanisms on board of mobile terminals will improve the video-streaming experience and offer a novel dimension to optimize caching policies in a site-dependent fashion. In this paper we present new rate-aware caching schemes able to optimize the cache composition both versus popularity of video contents and the aggregated users' video rate demand. Several algorithms are proposed and compared against their rate-agnostic counterparts. Extensive numerical results provide evaluation of the relative advantage of this new class of caching policies.

HTTP Adaptive Streaming is able to dynamically match video quality to variable network conditions. This is a key feature for multimedia delivery when quality of service cannot be granted network-wide. For instance, the end-to-end throughput towards mobile terminals may suffer short term fluctuations due to fading. Hence, robust bitrate adaptation schemes become crucial in order to avoid degraded video reproduction. The objective, in this context, is to control the filling level of the playback buffer, maximize the video quality, and avoid unnecessary quality variations which may also impair the perceived quality of experience. In this work we study bitrate adaptation algorithms leveraging on Backward-Shifted Coding (BSC), a scalable video coding scheme able to cope with the effects of end-to-end throughput fluctuations. We have proposed a new adaptation scheme able to balance video rate smoothness and high network capacity utilization. Both the throughput-based and buffer-based variants of such scheme have been designed. Extensive simulations using synthetic and real-world video traffic traces show that the proposed solution performs remarkably well even under challenging network conditions.

We take an analytical approach to study the Quality of user Experience (QoE) for video streaming applications. Our propose is to characterise buffer starvations for streaming video in the presence of bandwidth that varies over time. This variation of the bandwidth is caused due to the frequent changes of channel states and mobility. Specifically we develop an analytical framework to investigate Quality of user Experience (QoE) for streaming by considering a Markov Modulated Fluid Model (MMFM) that accurately approximates the nature of network traffic. We drive the close-form expressions for calculating the distribution of starvation as well as start-up delay using partial differential equations (PDEs) and solve them using the Laplace Transform. We illustrate the results with the cases of the two-state Markov Modulated Fluid Model that is commonly used in multimedia applications. We compare our analytical model with simulation results using ns-3 under various operating parameters. Finally, we apply our analysis results to optimize the objective quality of experience (QoE) of media streaming realizing the tradeoff among different metrics incorporating user preferences on buffering ratio, startup delay and perceived quality.

Although HTTP-based video streaming can easily penetrate firewalls and profit from Web caches, the underlying TCP may introduce large delays in case of a sudden capacity loss. To avoid an interruption of the video stream in such cases we propose the Backward-Shifted Coding (BSC). Based on Scalable Video Coding (SVC), BSC adds a time-shifted layer of redundancy to the video stream such that future frames are downloaded at any instant. This pre-fetched content maintains a fluent video stream even under highly variant network conditions and leads to high Quality of Experience (QoE). We characterize this QoE gain by analyzing initial buffering time, re-buffering time and content resolution using the Ballot theorem. The probability generating functions of the playback interruption and of the initial buffering latency are provided in closed form. We further compute the quasi-stationary distribution of the video quality, in order to compute the average quality, as well as temporal variability in video quality. Employing these analytic results to optimize QoE shows interesting trade-offs and video streaming at outstanding fluency., 9 pages