Cellular networks have witnessed the emergence of the HTTP Adaptive Streaming (HAS) as a new video delivery method. In HAS, several qualities of the same videos are made available in the network so that clients can choose the best quality that fits their bandwidth capacity. This has particular implications on caching strategies with respect to the viewing patterns and the switching behavior between video qualities. In this paper we present analysis of a real HAS dataset collected in France and provided by the country's largest mobile phone operator. Firstly, we analyse the viewing patterns of HAS contents and the distribution of the encoding bit rates requested by mobile clients. Secondly, we give an in-depth analysis of the switching pattern between video bit rates during a video session and assess the implication on the caching efficiency. We also model this switching based on empirical observations. Finally, we propose WA-LRU a new caching algorithm tailored for HAS contents and compare it to the standard LRU. Our evaluations demonstrate that WA-LRU performs better and achieves its goals.

HTTP Adaptive bitrate video Streaming (HAS) is now widely adopted by Content Delivery Network Providers (CDNPs) and Telecom Operators (Telcos) to improve user Quality of Experience (QoE). In HAS, several versions of videos are made available in the network so that the quality of the video can be chosen to better fit the bandwidth capacity of users. These delivery requirements raise new challenges with respect to content caching strategies, since several versions of the content may compete to be cached. In this paper we present analysis of a real HAS dataset collected in France and provided by a mobile telecom operator involving more than 485,000 users requesting adaptive video contents through more than 8 million video sessions over a 6 week measurement period. Firstly, we propose a fine-grained definition of content popularity by exploiting the segmented nature of video streams. We also provide analysis about the behavior of clients when requesting such HAS streams. We propose novel caching policies tailored for chunk-based streaming. Then we study the relationship between the requested video bitrates and radio constraints. Finally, we study the users' patterns when selecting different bitrates of the same video content. Our findings provide useful insights that can be leveraged by the main actors of video content distribution to improve their content caching strategy for adaptive streaming contents as well as to model users' behavior in this context.

HTTP-based delivery for Video on Demand (VoD) has been gaining popularity within recent years. Progressive Download over HTTP, typically used in VoD, takes advantage of the widely deployed network caches to release video servers from sending the same content to a high number of users in the same VoD service. However, due to the inherent heterogeneity of user demands, which may result in requesting the same video content in different resolutions or qualities, the caching efficiency is expected to decrease due to a higher variety in requested media files. The use of Scalable Video Coding allows different representations of the same content to be combined in a single file, whose parts, aka layers, are requested sequentially by a user up to the maximum desired quality. In this paper we show the benefits of using Scalable Video Coding to maintain the same set of possible video content representations, while at the same time maximizing the caching efficiency.

HTTP-based delivery for Video on Demand (VoD) has been gaining popularity within recent years. Progressive Download over HTTP, typically used in VoD, takes advantage of the widely deployed network caches to relieve video servers from sending the same content to a high number of users in the same access network. However, due to a sharp increase in the requests at peak hours or due to cross-traffic within the network, congestion may arise in the cache feeder link or access link respectively. Since the connection characteristics may vary over the time, with Dynamic Adaptive Streaming over HTTP (DASH), a technique that has been recently proposed, video clients may dynamically adapt the requested video quality for ongoing video flows, to match their current download rate as good as possible. In this work we show the benefits of using the Scalable Video Coding (SVC) for such a DASH environment.

The aim of this paper is to analyze the performance of a large number of long-lived TCP controlled flows sharing many routers (or links), from the knowledge of the network parameters (capacity, buffer size, topology) and of the characteristics of each TCP flow (RTT, route etc.) when taking synchronization into account. It is shown that in the small buffer case, the dynamics of such a network can be described in terms of iterate of random piecewise affine maps, or geometrically as a billiards in the Euclidean space with as many dimensions as the number of flow classes and as many reflection facets as there are routers. This class of billiards exhibits both periodic and nonperiodic asymptotic oscillations, the characteristics of which are extremely sensitive to the parameters of the network. It is also shown that for large populations and in the presence of synchronization, aggregated throughputs exhibit fluctuations that are due to the network as a whole, that follow some complex fractal patterns, and that come on top of other and more classical flow or packet level fluctuations. The consequences on TCP's fairness are exemplified on a few typical cases of small dimension.

The aim of this paper is to analyze the performance of a large number of long lived TCP controlled flows sharing many routers (or links), from the knowledge of the network parameters (capacity, buffer size, topology) and of the characteristics of each TCP flow (RTT, route etc.) when taking synchronization into account. It is shown that the dynamics of such a network can be described in terms of iterate of random piecewise affine maps, or geometrically as a billiards in the Euclidean space with as many dimensions as the number of flow classes and as many reflection facets as there are routers. This class of billiards exhibits both periodic and non-periodic asymptotic oscillations, the characteristics of which are extremely sensitive to the parameters of the network. It is also shown that for large populations and in the presence of synchronization, aggregated throughputs exhibit fluctuations that are due to the network as a whole, that follow some complex fractal patterns, and that come on top of other and more classical flow or packet level fluctuations. The consequences on TCP's fairness are exemplified on a few typical cases of small dimension.

The aim of this paper is to simulate the interaction of a large number of TCP controlled flows and UDP flows sharing many routers/links, from the knowledge of the network parameters (capacity, buffer size, topology, scheduling) and of the characteristics of each TCP (RTT, route etc.) and UDP flow. This work is based on the description via some fluid evolution equations, of the joint evolution of the window sizes of all flows over a single bottleneck router/link, as function of the synchronization rate. It is shown that the generalization of this fluid dynamics to a network composed of several routers can be described via equations allowing one to simulate the interaction of e.g. millions of TCP flows on networks composed of tens of thousands of links and routers on a standard workstation. The main output of the simulator are the mean value and the fluctuations of the throughput obtained by each flow, the localization of the bottleneck routers/links, the losses on each of them and the time evolution of aggregated input traffic at each router or link. The method is validated against NS simulations. We show that several important statistical properties of TCP traffic which were identified on traces are also present on traffic generated by our simulator: for instance, aggregated traffic generated by this representation exhibits the same short time scale statistical properties as those observed on real traces. Similarly, the experimental laws describing the fairness of the bandwidth sharing operated by TCP over a large network are also observed on the simulations.

We propose a natural and simple model for the joint throughput evolution of a set of TCP sessions sharing a common tail drop bottleneck router, via products of random matrices. This model allows one to predict the fluctuations of the throughput of each session, as a function of the synchronization rate in the bottleneck router; several other and more refined properties of the protocol are analyzed such as the instantaneous imbalance between sessions, the autocorrelation function or the performance degradation due to synchronization of losses. When aggregating traffic obtained from this model, one obtains, for certain ranges of the parameters, short time scale statistical properties that are consistent with a fractal scaling similar to what was identified on real traces using wavelets.