Congestion control mechanisms for new network transmission protocols or peer-to-peer applications must interact well with Transmission Control Protocol (TCP).
TCP throughput unfairness over WiFi is a critical problem and needs further investigations.
[1] Raj Jain's equation, rates the fairness of a set of values where there are
This metric identifies underutilized channels and is not unduly sensitive to atypical network flow patterns.
In packet radio wireless networks, The fairly shared spectrum efficiency (FSSE) can be used as a combined measure of fairness and system spectrum efficiency.
The system spectral efficiency is the aggregate throughput in the network divided by the utilized radio bandwidth in hertz.
The FSSE is the portion of the system spectral efficiency that is shared equally among all active users (with at least one backlogged data packet in queue or under transmission).
In case of scheduling starvation, the FSSE would be zero during certain time intervals.
FSSE is useful especially when analyzing advanced radio resource management (RRM) schemes, for example channel adaptive scheduling, for cellular networks with best-effort packet data service.
In such system it may be tempting to optimize the spectrum efficiency (i.e. the throughput).
However, that might result in scheduling starvation of "expensive" users at far distance from the access point, whenever another active user is closer to the same or an adjacent access point.
Thus the users would experience unstable service, perhaps resulting in a reduced number of happy customers.
Optimizing the FSSE results in a compromise between fairness (especially avoiding scheduling starvation) and achieving high spectral efficiency.
If the cost of each user is known, in terms of consumed resources per transferred information bit, the FSSE measure may be redefined to reflect proportional fairness.
This policy is less fair since "expensive" users are given lower throughput than others, but still scheduling starvation is avoided.
The idea of QoE fairness is to quantify fairness among users by considering the Quality of Experience (QoE) as perceived by the end user.
Several approaches have been proposed to ensure network-wide QoE fairness especially for adaptive video streaming.
Hence, fairness measures like Jain's fairness index cannot be applied, as the measurement scale requires to be a ratio scale with a clearly defined zero point (see examples of misuse for coefficients of variation).
Hossfeld et al. have proposed a QoE Fairness index which considers the lower bound
with 1 indicating perfect QoE fairness – all users experience the same quality.
0 indicates total unfairness, e.g. 50% of users experience highest QoE
As Jain's fairness index is said to be unduly sensitive under atypical conditions, the product-based fairness can be defined arbitrarily to obtain a desired sensitivity.
, this gives a minimum to maximum ratio of about The linear product-based fairness index has
is primarily used by telecom operators in the context of bandwidth allocation[citation needed].
The first quadrant of the sine wave is used as a mapping function to inflate fractions .
As such, the sensitivity of the product-based fairness is decreased for values close to
Compared to Jain's fairness index, G's fairness index yields smaller values, it is more sensitive to potential unfair bandwidth distribution and can go to zero.
In the context of networks, the latter is an advantage over Jain's fairness index when a few values in a set drop to low levels.
, the Bossaer's fairness index inflates the fractions closer to 0.
Causal fairness measures the frequency with which two nearly identical users or applications who differ only in a set of characteristics with respect to which resource allocation must be fair receive identical treatment.