Streaming video continues to experience unprecedented growth. This underscores the need to identify user-centric performance measures and models that will allow operators to satisfy requirements for cost-effective network dimensioning delivered with an acceptable level of user experience. This paper presents an analysis of two novel metrics in the context of fixed capacity links: (i) the average proportion of a video’s playing time during which the quality is reduced and (ii) the average proportion of videos which experience reduced quality at least once during their playing time, based on an M/M/∞ system. Our analysis is shown to hold for the more general M/G/∞ system for metric (i), but not for (ii) and simulation studies show an unexpected form of sensitivity of metric (ii) to the flow duration distribution, contrary to the norm of increasing variance causing worse performance. At typical operational loads these new metrics provide a more sensitive and information rich guide for understanding how user experience degrades, than the widely used average throughput metric does. We further show that only the combined use of this existing and our new metrics can provide a holistic perspective on overall user performance.

This paper presents our work regarding the comparison of the behaviour of some of the major TCP variants in a LTE network. Using the LENA LTE simulator and the DCE framework, the behaviour of the Linux implementations of seven TCP variants are compared. The evolution of the throughput, congestion window and queuing delay are studied for four scenarios with different network loads and flow types. Our measurements show that, in the situations we consider, most variants are able to quickly reach full link utilisation. However, to reach the same throughput, they create different amounts of queuing delay. On the one hand, loss based algorithms tend to totally fill the queue, creating huge queuing delays and inducing packet losses. On the other hand, delay based variants manage to limit the queue size and decrease the amount of packet dropped by the eNodeB but struggle to reach the maximum throughput in some circumstances.

Mobile internet has been widely adopted and it is expected to rise to almost 4 billion users by 2020. Despite the research effort dedicated to the enhancement of its performance, there still exists a gap in the understanding of how TCP and its many variants work over LTE. To this end, this paper evaluates the extent to which five common TCP variants, CUBIC, NewReno,Westwood+, Illinois, and CAIA Delay Gradient (CDG), are able to utilise available radio resources under hard conditions, such as during start-up and in mobile scenarios at different speeds. The paper suggests that CUBIC, due to its Hybrid Slow-Start mechanism, enters congestion avoidance prematurely, and thus experiences a prolonged start-up phase. As a result, it is unable to efficiently utilise radio resources during shorter transmissionsessions. Besides, CUBIC, Illinois and NewReno, i.e., the loss-based TCP implementations, offer better throughput, and are able to better utilise available resources during mobility than Westwood+ and CDG - the delay-based variants do.

We investigate the potential of different pre-fetching and/or caching strategies for different user behaviour with respect to surfing or browsing in a catch-up-TV network. To this end we identify accounts and channels associated with strong or weak surfing or browsing respectively and study the distributions of hold times for the different types of behaviour. Finally we present results from a request prediction model and a caching simulation for the different types of behaviour and find that the results are relatively similar.

TV-on-Demand service has become one of the most popular Internet applications that continuously attracts higher user interests. With rapidly increasing user demand, the existing network conditions may not be able to ensure low start-up delay of video playback. Prefetching has been broadly investigated to cope with the start-up latency problem which is also known as user perceived latency. In this paper, we analyse request patterns for TV programs from a popular Swedish TV service provider over 11 weeks. According to the analysis, we propose a prefetching scheme at the user end to preload videos before user requests. Our prefetching scheme significantly improves the cache hit ratio compared to terminal caching and we note that there is a potential to further improve prefetching performance by customizing prefetching schemes for different video categories. We further present a cost model to determine the optimal number of videos to prefetch. Finally, we discuss available time for prefetching and suggest that when to make prefetching decisions depends on the user demand patterns of different video categories.

The coverage areas of cellular networks are logically subdivided into service areas. Each service area has a local anchor node which “hides” the mobility inside the area and the entire network has a global anchor node which “hides” the mobility between areas.

The concept of unique local anchor nodes per service area was invented to simplify routing but has been found to complicate expansion. The rapidly growing demand for cellular access has therefore prompted for alternative solutions with pools of local anchor nodes per service area. Such pools are now deployed by several operators all over the world.

Users in pooled service areas are mapped to specific pool members according to a load distribution policy, but the mapping can change as a result of node failures or operator interventions. Such changes take a certain time to implement and cause additional load on the anchor nodes. We study these processes in detail and derive closed form expressions which allow operators to control the trade-off between rapid changes and acceptable loads.

Finally we show that the key assumptions of our model are in agreement with measured data and demonstrate how the model can be applied to investigate the effects of different network settings (timers) under different user behaviour (traffic and mobility).

Contrary to current solutions to this problem, which typically are slow and/or inaccurate, our results enable fast and accurate analysis of different scenarios thereby enabling operators to maximise utilisation of the existing investments and at the same time avoid potentially dangerous situations of overload.

TV-on-Demand services have become one of the most popular Internet applications that continuously attracts high user interest. With rapidly increasing user demands, the existing network conditions may not be able to ensure a low start-up delay of video playback. Prefetching has been broadly investigated to cope with the start-up latency problem, which is also known as user perceived latency. In this paper, two datasets from different IPTV providers are used to analyse the TV program request patterns. According to the results, we propose a prefetching scheme at the user end to preload videos before user requests. For both datasets, our prefetching scheme significantly improves the cache hit ratio compared to passive caching and we note that there is a potential to further improve prefetching performance by customizing prefetching schemes for different video categories. We further present a cost model to determine the optimal number of videos to prefetch. We also discuss if there is enough time for prefetching. Finally, more factors, which may have an impact onoptimizing prefetching performance, are further discussed, such as the jump patterns over different time in a day and the the distribution of each video’s viewing length.

Data volumes in communication networks increase rapidly. Further, usage of social network applications is very wide spread among users, and among these applications, Facebook is the most popular. In this paper, we analyse user demands patterns and content popularity of Facebook generated traffic. The data comes from residential users in two metropolitan access networks in Sweden, and we analyse more than 17 million images downloaded by almost 16,000 Facebook users. We show that the distributions of image popularity and user activity may be described by Zipf distributions which is favourable for many types of caching. We also show that Facebook activity is more evenly spread over the day, compared to more defined peak hours of general Internet usage. Looking at content life time, we show that profile pictures have a relatively constant popularity while for other images there is an initial, short peak of demand, followed by a longer period of significantly lower and quite stable demand. These findings are useful for designing network andQoE optimisation solutions, such as predictive pre-fetching, proxy caching and delay tolerant networking.

In this work, the performance of 5 representative caching replacement policies was investigated and compared for caching Internet video-on-demand (VoD) in local access networks. Two measured traces of end-user requests were used in the analyses for two typical VoD services: TV-on-demand and user generated content represented by YouTube. The studied policies range from simple least recently used (LRU) and least frequently used (LFU) algorithms to more advanced ones denoted as LFU-dynamic lifespan (LFU-DL), Adaptive replacement cache (ARC) and Greedy-dual size frequency (GDSF). Our results show that the ARC policy always outperforms the other policies due to its adaptive nature and its ability to track changes in the traffic patterns. On the other hand, the simple LRU policy can also achieve a caching performance which is comparable to that of the more advanced ARC policy especially for the TV-on-demand service when the potential caching gain is high. On the contrary, the simple LFU policy always shows the poorest performance.However, by applying a proper lifespan supplement under the LFU-DL policy, the caching performance can be effectively enhanced to the level achievable using ARC and LRU policies. Moreover, the GDSF policy does not outperform simple LRU or LFU-DL, especially for YouTube video clips when the potential caching gain is relatively low. The advantage of GDSF manifested in our analysis is, however, its outstanding cache space usage efficiency among the five studied caching algorithms.

In this paper we study traffic patterns in a large municipal WiFi network and in particular those of the most bandwidth hungry application, viz. YouTube, for which we provide a detailed analysis of demand in different geographical areas and over time. We consider the possibilities to reduce network traffic and increase Quality of Experience (QoE) by serving repeated requests for YouTube videos from caches placed either at the network head end, at the wireless access points, or in the user devices. Our data confirms that a significant part of the YouTube traffic can be served by such devices and that there exists a potential to optimize caching performance by exploiting the content demand locality. We also discover a previously unknown pattern of periodicity in content demand and present a simple example of how to exploit this in cache design.