will have lower interest in providing high QoS. When the demand for ser- vices that require high QoS is comparable with demand for services not requiring QoS, then the WTP for QoS is higher. For example, consumers have WTP for QoS for IPTV (as a replacement for traditional TV), VoIP (as a replacement for PSTN/ISDN), busi- ness services (e.g. VPNs as replacement of leased lines), critical IoT services (which can be provided without QoS, such as smart transportation or industry), as well as related future smart services (e.g. smart cars, smart homes, smart sustainable cities). So, many services that demand QoS to function cannot be delivered without QoS, such as the best-effort and network neutral approach which is used for IASs. Thus WTP for ser- vices that demand high QoS is higher; however, the number of customers that have such higher WTP is lower than the number of customers with lower (i.e. moderate) WTP. Considering a single service, such as IAS, different WTP by different customers may results in different offers, such as a bigger data cap for customers with high WTP and a lower data cap for IAS customers with lower WTP. Due to lower price per unit in the wholesale, the price per volume of data (e.g. per GB) is lower for bigger data caps than for smaller data caps, SO customers with higher WTP at the end will pay less per gigabyte than customers with lower WTP. Similarly, if one considers fixed broadband and ultra-broadband IAS (e.g. based on fiber in the access part), the bitrates can also be used as the main QoS parameter (together with the data cap, e.g. on a monthly basis) for differentiation of users with different WTP. Users with lower WTP will get IAS with lower speeds, while users with higher WTP will get IAS with higher speeds. If there are many customers for the given service then it is likely that their behavior will result in a normal (i.e. Gaussian) probability distribution function (as a result of the Central Limit Theorem from probability theory, according to which when independent random var