Dave Cecil Robinson

Optimum caching strategies for a telco CDN

In order to cope with the rapidly increasing video traffic (both downloaded and streamed) resulting from Internet TV, a telecommunications company (telco) can deploy a network of caches, which sit closer to the user than the point-of-presence of a traditional content distribution network (CDN), like Akamai or Limelight Networks. This network of highly distributed caches, often referred to as a telco CDN, is a network enabler that saves a huge amount of traffic on the links toward the Internet by handling user requests locally without having to retrieve data from the origin server. Because for Internet TV the set of available content is huge and rapidly evolving, the choice of the caching algorithm and cache collaboration strategy can have a considerable impact on the reduction in traffic. The caching algorithm decides which objects to cache, and the cache collaboration strategy, at times in combination with preloading, determines how cacheable items are propagated throughout the telco CDN. In this paper we determine the best combination of a caching algorithm and a cache collaboration strategy for a telco CDN.

QoE model for video delivered over an LTE network using HTTP adaptive streaming

The end user quality of experience (QoE) of content delivered over a radio network is (mainly) influenced by the radio parameters in the radio access network. This paper will present a QoE model for video delivered over a radio network (e.g., Long Term Evolution (LTE)) using HTTP (Hypertext Transfer Protocol) adaptive streaming (HAS). The model is based on experiments performed in the context of the Next Generation Mobile Networks (NGMN) project P-SERQU (Project Service Quality Definition and Measurement). In the first phase, a set of representative HAS profiles were selected based on a lab experiment where scenarios with typical radio impairments (fading, signal-to-interference-plus-noise ratio, round trip time and competing traffic) were investigated in a test network. Based on these HAS profiles, video files were prepared by concatenating chunks of the corresponding video quality. In a second phase, these video files were downloaded, viewed, and rated by a large number of volunteers. Based on these user scores a mean opinion score (MOS) was determined for each of the video files, and hence, the HAS profiles. Several QoE models that predict the MOS from the HAS profile have been analyzed. Using the preferred QoE model, a range of MOS values can be obtained for each set of initial radio impairments. It is argued that a QoE model based on the radio parameters is necessarily less accurate than a QoE model based on HAS profiles and an indication is given of how much the performance of the former is less than the latter.

Interaction of AQM schemes and adaptive streaming with Internet traffic on access networks

We tested AQM techniques on their ability to influence end-user QoE, especially HTTP Adaptive Streaming (HAS) based video traffic on fixed access networks. Using ns-2 we built a set of simulation scenarios for realistic Internet traffic. Based on data from recent Internet measurements, there are three major types of traffic flowing through the Internet: HTTP web traffic; HAS video traffic and progressive download (PD) video traffic. We modeled HTTP web traffic using Internet statistics published by Google. We generated dynamic HAS traffic by implementing a HAS client. We modeled PD video traffic using statistics from Youtube. We used published research to convert data from simulation traces into QoE metrics. We used realistic mixes of the three types of traffic with different loading conditions to test Random Early Detection (RED), CoDel and Tail-drop queuing on DSL access networks. We also tested various options in HAS for their impact on its QoE. Most AQM configurations improved the QoE metrics of HAS and web traffic. They improved fairness among HAS streams, avoided HAS underflow in a majority of the cases where it would occur with Tail-drop, and tangibly reduced the latency for web page downloads. Sensitivity analysis with RED parameters confirmed that the improved QoE of HAS and web traffic was relatively insensitive to the exact RED parameter set.

Application enablement: A path to new opportunities for future services

Application enablement opens the door to higher value experiences for users and higher revenue opportunities for providers of communication services. The concept is to expose elements that have value–whether information or control or a task/service–to application developers for the purpose of enabling richer applications, which can be Web-based, mobile handset–based, or a combination.