Mobile communications networks are an increasingly popular way for their users to communicate with one another. The number of mobile subscribers is increasing, and with more and more applications and media content available to the subscribers, the amount of data traffic that each subscriber sends or receives is also increasing. Increasing numbers of subscribers and increasing amounts of data traffic place a burden on mobile communications networks. Mobile networks have limitations on their capacity per geographical area (e.g., cell), and so require careful distribution of resources for data traffic.
If the amount of data traffic in a mobile network exceeds a certain limit, then the mobile network can become congested, as the available radio resources, as well as other resources, are finite. Congestion impacts the quality of service received by subscribers if data becomes dropped or delayed. Furthermore, congestion can impact the total amount of traffic carried by the network, since certain applications that get lower access rate in peak hours might not be possible to compensate at times when there is free capacity.
Some client applications can tolerate delays of up to several seconds, minutes or even hours. For example, a subscriber may wish to download a movie for later viewing. The subscriber may wait until a time when there is no congestion in the network before beginning the download, to ensure that they get a faster download rate. Similarly, in machine to machine (M2M) applications, it may be acceptable for sensors reporting measurement to delay transmission to a period when there is no congestion in the network. A third example is an application that regularly generates keep-alive messages. Keep-alive messages cannot be delayed for hours, but can typically be delayed for several seconds without adversely affecting the application. A further example of data traffic that can tolerate a delay is regular news updates to the subscriber.
In order to best utilize the network resources, and to ensure that all subscribers get a required best Quality of Service (QoS) if they are sending or receiving data traffic (such as a live media stream) which cannot tolerate delay, it would be beneficial to reduce congestion by avoiding transmitting data traffic that can tolerate some delay at congestion periods, and to transmit such data traffic them at low-traffic periods instead.
A simple way to address the problem of congestion is for network operators to update their networks and provide more bandwidth and hardware to handle the increase in data traffic. This solution is expensive.
Another known way to address the problem is to limit data traffic of a certain type that is known to be bandwidth-intensive. For example, peer to peer (P2P) data traffic typically consumes a lot of resources, making those resources unavailable for other applications such as Web browsing. Operators therefore typically install certain tools/logic/policy control into their system which throttles P2P traffic during predetermined times of congestion. This works by limiting the total amount of bandwidth available to P2P traffic to a relatively low value for all users in a particular cell during the predetermined times of congestion.
A problem with P2P throttling is that it is performed on a whole class of traffic, and is not performed on the basis of an individual subscriber, cell, or time-of-day basis. Instead, throttling is applied to all subscribers in all cells in the predetermined time period. P2P traffic is not throttled dynamically, so cannot take account of congestion or QoS degradation in periods outside the predetermined time period.
Another approach to mitigate the problem of congestion, described in WO 2005/022941, is to explicitly define a download time for a not so urgent object download. The download time is set in advance by the user manually, for example to a night hour when congestion is less likely. Some operators provide financial incentives for users to schedule their data traffic at low-traffic periods. For example, in mobile networks that charge per minute, prices are typically lower at night, when congestion is less likely, than during the day.
Many subscribers schedule their data traffic at low-traffic time periods manually, simply to obtain faster data traffic rates. For example, P2P users who notice that data transfer speeds are higher in the early morning hours can schedule their P2P applications accordingly.
The delayed download mechanisms rely on the subscriber's knowledge of times of low congestion or times when per-minute charging is lower. If the download time is set in advance, there is no guarantee that there will be no congestion at the set time.