The volume of data traffic potentially available for off-load

In this chapter, we discuss, the traffic potentially available for off-load (and some newly identified challenges in estimating it), together with the factors influencing the level of off-load. Traffic off-load is far more widespread than we would have assumed at the outset of the study, as we explain in Section 4.1. Our estimates of the volume appear in Section 4.2.

4.1 Surprisingly rapid adoption of off-load

Our expectation at the outset of the study, based in part on estimates of the level of traffic off-load from sources such as the Cisco VNI (2012), was that take-up of traffic off-load by consumers would prove to be small relative to the total volume of mobile traffic, largely due to possible inconvenience of enabling off-load. As we explain in this chapter, the data sources that we have studied seem to demonstrate instead that consumer use of traffic off-load is extremely widespread.

For the end-user, traffic off-load may require minor reconfiguration of the end-user device. For a device in the home, the user must be aware of the option of using a Wi-Fi router that often is already installed, and needs to configure the end-user device (e.g.

smartphone) to recognise the router (which may involve setting an SSID, a password, whatever). Multiple sources suggest that most European consumers who have smart phones, under the strong incentives of capacity limitations, data caps, and tiered pricing plans, have already surmounted this hurdle, as we explain shortly.

When a user is moving or travelling, even if an off-load capability is available, its use depends not only on the ability of the device to recognise and use the capability, but also on a commercial arrangement with the provider. The most familiar example is nomadic use of Wi-Fi in one’s hotel room. Many users are now experienced with configuring their laptop computers to find the hotel’s Wi-Fi service, and with entering a user id and password so as to ensure that payment proceeds properly.

As noted in Section 2.8, technological and market solutions have emerged. Techniques such as MAC authentication and Passpoint™ can greatly simplify authentication and hand-over for public data off-load services.

Apparently, users are getting past these impediments, at least for private (e.g. home) Wi-Fi use, and we feel that they are likely to get past them for public Wi-Fi use as well.

In the case of Android smart phones, a recent study by Informa and Mobidia found that the use of Wi-Fi exceeded 90% in developed European countries.

Figure 4-1: Percentage of Android phone users who use Wi-Fi (January 2013)

The same Informa / Mobidia study found a significant number of Android smart phone users who had connected at least once during the month of January 2013 to a public Wi-Fi service; however, while the numbers varied greatly from one country to the next, in no case were public (managed) Wi-Fi connections as widely prevalent as private (self-provisioned) Wi-Fi connections. In any case, it appears that users are able to get past the user convenience impediments.

Figure 4-2: Managed vs. self-provisioned Wi-Fi adoption by country (January 2013)

One might have anticipated that these user convenience issues would be analogous to those that pertain to international mobile roaming. Work-arounds to high mobile roaming costs have been available for a decade, but the tendency has been for only highly motivated and highly knowledgeable consumers to avail themselves of them. Work-arounds to expensive mobile roaming have tended to be a niche solution, not a mass market solution.

Based on usage data such as that depicted in Figure 4-1 and Figure 4-2, however, traffic off-load solutions seem to be following a quite different trajectory than that of international mobile roaming solutions. The technical solutions seem to be sufficient, and consumers are motivated to use them.

4.2 Estimating the magnitude of traffic off-load

The starting point for this part of the work is an estimation of the volume of data traffic that would be mobile in the absence of any off-load. In network design and analysis, one typically starts with an estimate of the offered load that would be present in the absence of constraints.

The Cisco Mobile VNI data represents, in our view, a good estimate of mobile data traffic net of off-load; however, we have less confidence in their estimates of the amount of mobile data off-load, which neither the fixed nor the mobile operators can directly measure. Our approach has therefore been to “triangulate” among multiple data sources in order to obtain a clearer picture of the level of off-load already taking place.

Our analysis reflects groupings of the “Big Five” Member States: Germany, France, the UK, Spain, and Italy. Data at Member State level does not appear in Cisco’s publications, but data on these Member States can be extracted from their online database. The Annex to this report contains figures that depict the results for Germany, Spain, the UK, and Italy.³⁸

Our time window for this study runs until 2020 and beyond, while Cisco data covers the period to 2016 or 2017. We have therefore extrapolated the Cisco data forward using a so-called “logistic curve”. This is a standard technique in forecasting technology take-up. The logistic curve recognises that year over year growth does not remain constant in percentage terms, but tends to decline as products and services reach maturity and markets approach saturation. This has visibly been the case with Internet fixed traffic,³⁹ and appears to be the case for mobile Internet data as well. A projection to 2020 of total mobile traffic for Germany, for example, appears in Figure 4-3.

38 Note that the data used was viewed in March and April 2013, and did not yet reflect the revisions of February, 2013.

39 See for instance J. Scott Marcus and Dieter Elixmann: "Re-thinking the Digital Agenda for Europe (DAE): A richer choice of technologies", report on behalf of Liberty Global, September 2012, available at: http://www.lgi.com/PDF/public-policy/LGI-report-Re-thinking-the-Digital-Agenda-for-Europe.pdf.

Figure 4-3: Cisco VNI estimates for mobile Internet traffic in Germany projected forward to 2020

Source: Cisco VNI (2012) data, WIK calculations

Our assessment of spectrum needs later in the report reflects logistics curve extrapolations of this type to 2025 for France, Germany, Italy, Spain and the UK.

The Cisco VNI includes a rough estimate of the degree of data traffic off-load; however, their estimate of data traffic off-load does not suffice for our purposes. First, they provide only a global estimate, while we require country-specific estimates that clearly cannot be assumed to be the same; second, their estimate seems to be implausibly low, at least in regard to some of the more advanced European Member States.⁴⁰ We have therefore developed our own Member State-specific estimates.

The level of off-load that is possible clearly depends on many factors, some of which are in rapid flux just now, including:

40 Given the lower presence of Wi-Fi hotspots in some other regions of the world, and the lower penetration of the fixed network in developing countries, the estimate may possibly be correct for the world as a whole.